{"as_of":"2026-08-10T14:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f997a79da3d43e636b4e08aa39579ff07bba09942e7f8f241309577abd5e0f41","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:47:36.183936Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.21499/citation-record","integrity":"/paper/2507.21499/integrity","json":"/paper/2507.21499/citation-record.json","paper":"/paper/2507.21499"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:38.250480Z","title":"Bungeenerf: Progressive neural radiance field for extreme multi- scale scene rendering,","venue":null,"work_id":"7ea3ff9e-c4e1-4a29-a20e-a12525e4ac28","year":2022},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.601390Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:7302cebb0db4828a2dff24edb82eb665e61963320a651a348aeb8cf49c623376","observation_id":"0f4928f3-7069-424b-9ecd-0007add12cdd","resolution":{"observed_at":"2026-08-06T12:47:38.260630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:38.213849Z","title":"Mip-nerf: A multiscale representation for anti- aliasing neural radiance fields,","venue":null,"work_id":"448fdcb1-25a7-4742-a717-44f908691cc4","year":2021},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.609040Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:b70c48537bffd2f5ee3716b0b20549777dc5b6749825bbac12cb6c5ec890273e","observation_id":"32dfef92-b1ff-4f2c-bdcc-d6ba82e1ad50","resolution":{"observed_at":"2026-08-06T12:47:38.225672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:38.184795Z","title":"Bacon: Band-limited coordinate networks for multiscale scene representation,","venue":null,"work_id":"d2aa95ab-15ed-47e9-86a1-88c850b65ae5","year":2022},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.617376Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:1ae1fd87da0459db2e5eb2a068ead1abc6cf9e3ddf67f2f87cc78a857578c27b","observation_id":"dfb1b676-bfd8-4572-a830-0a20bb78a830","resolution":{"observed_at":"2026-08-06T12:47:38.192997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:38.145705Z","title":"Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures,","venue":null,"work_id":"443cbd9c-a527-42f3-ac39-d72b118f54a3","year":2023},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.626846Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:de393213878d58c90660e1973ae9acea4f71490eef6df63804e87b50964135ab","observation_id":"afd1168a-958a-4fd3-a99a-10a4f8b1a554","resolution":{"observed_at":"2026-08-06T12:47:38.161246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:38.109886Z","title":"Efficientnerf efficient neural radiance fields,","venue":null,"work_id":"f4e2fbd5-74c2-4728-b2b0-88effc42ca1a","year":2022},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.635527Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:443fff4c68635fd3406a8e75024f6084777da07fa6318b472fb806ebce0b0f75","observation_id":"c95197c4-7f36-4ba1-97a7-73d966df9856","resolution":{"observed_at":"2026-08-06T12:47:38.119497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:38.061612Z","title":"Baking neural radiance fields for real-time view synthesis,","venue":null,"work_id":"92ca9573-7198-40cf-9573-769c92259478","year":2021},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.645600Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:425bb81e57069a43fdce900b845335d9266edc78938f541da0a1ad192cd3cf7b","observation_id":"3730ee38-4b66-44fd-96d2-fe1c2d0400bd","resolution":{"observed_at":"2026-08-06T12:47:38.079021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:38.017597Z","title":"Driv- inggaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes,","venue":null,"work_id":"a766a727-d9e4-4d6d-a656-2f501b8331b4","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.657355Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:af436df68a10fb178ece90156644c6d21657d8b7095ea35dc6699ea2517831c0","observation_id":"8019bf2f-7729-42c5-bd80-5cb1f42ddc40","resolution":{"observed_at":"2026-08-06T12:47:38.022795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.975800Z","title":"Gaussian splatting slam,","venue":null,"work_id":"d374ea08-a1f8-4d72-9eb1-6bbd0347b4d1","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.668950Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:58c8cd5071929d6316832aac7109759590b6e9765364dc01dad11ea97f48d290","observation_id":"0f9f94e1-ceba-47f1-83e1-04fd9e18cc29","resolution":{"observed_at":"2026-08-06T12:47:37.981466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.930979Z","title":"Gs-slam: Dense visual slam with 3d gaussian splatting,","venue":null,"work_id":"703317d9-f9b8-413f-9a1d-79cb1a203a47","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.679522Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:478ba77304a5793fb2000ebd1ea5440ae5de4b6fbb2fcc319bf227f77aba7a6f","observation_id":"522f9018-4f6b-4531-aa2f-e9b2aa6b3a66","resolution":{"observed_at":"2026-08-06T12:47:37.948040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.898847Z","title":"Nerf: Neural radiance field in 3d vision, a comprehensive review,","venue":null,"work_id":"c88ad2d6-8480-4157-a439-a650e21df935","year":2022},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.693136Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:a3d7f2cb34d6bd592c5d5656bfd9f4c0065e66ee8c39b609d8dce564ffd3bead","observation_id":"64b47419-d5b1-4485-8830-9c550b4de304","resolution":{"observed_at":"2026-08-06T12:47:37.905362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.858524Z","title":"A survey on 3d gaussian splatting,","venue":null,"work_id":"319eb457-dd0a-465d-b52b-0abd2138aea4","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.710674Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:309f58153d49d91e18fee11e697528104a65f5cbc8ffe0a8b0f1d609d1cb508c","observation_id":"a467fed0-1a30-4f05-9458-275699fcd7e9","resolution":{"observed_at":"2026-08-06T12:47:37.874683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.818879Z","title":"3d gaussian splatting for real-time radiance field rendering,","venue":null,"work_id":"8ca2922a-f93b-4f9c-93e0-772d246e869c","year":2023},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.739847Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:694008ec8107771fce7b9e645d2442f766a76c13a074a9483c0b362dc7cfd776","observation_id":"be086159-0292-4d90-a4e7-70c7f847e26b","resolution":{"observed_at":"2026-08-06T12:47:37.825104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.775711Z","title":"A hierarchical 3d gaussian representation for real-time rendering of very large datasets,","venue":null,"work_id":"638f001f-a408-4a3a-9c34-58054034bfc6","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.747377Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:994dc94c644c7738c688f8ffebe31518c4018edd4432cc5ca1b0b6322f9aa7b0","observation_id":"c7493b17-9470-44bb-ad12-849045610f8a","resolution":{"observed_at":"2026-08-06T12:47:37.787614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.744433Z","title":"Mini-splatting: Representing scenes with a constrained number of gaussians,","venue":null,"work_id":"523a7747-3dee-4811-810c-ee5c4fd50893","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.759086Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:41ff99044952a56043edfb9fa5f611a0c0f6052cf40297dad9349da575860f82","observation_id":"b45f34c2-0a63-4b2e-bfe4-419372090d5d","resolution":{"observed_at":"2026-08-06T12:47:37.758880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.699830Z","title":"Compact 3d gaussian representation for radiance field,","venue":null,"work_id":"01a93621-e4c6-4113-898a-b00ea7cc6691","year":2023},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.774958Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:9ca525344d46dabcafb7da772d3bae63b1170ec0dbd9a0526b6dedaaf65215bc","observation_id":"08a92fe8-3466-4008-8454-0620258f2a3b","resolution":{"observed_at":"2026-08-06T12:47:37.718229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.658011Z","title":"Recent advances in 3d gaussian splatting,","venue":null,"work_id":"e0552e01-9a33-4db7-8f9e-84e9ee8cbdcd","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.781823Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:cac2db6c53fc75e24b60de4f105367d2eba6e7908dac2b96cb93102485138523","observation_id":"c14e7ad0-5a96-4ac3-9e66-248c5b7088be","resolution":{"observed_at":"2026-08-06T12:47:37.675163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.626642Z","title":"Pharr, W","venue":null,"work_id":"8874da33-7e39-453c-a617-871de63f832b","year":2023},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.786536Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:ef41d6f2c9e49eaaecfbd86a7e5d473f0edd18b049f860444676d610e8b58952","observation_id":"698d241b-f68e-429a-af46-cff3c5d9ebfa","resolution":{"observed_at":"2026-08-06T12:47:37.636620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.590970Z","title":"Toward real-time ray tracing: A survey on hardware acceleration and microarchitecture techniques,","venue":null,"work_id":"ac90b554-5b0c-453c-a080-26e02d8f5be2","year":2017},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.793801Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:d0a2bebb79761abb8ed57d4377091ddd58c0c5f44c2697e09a69a4d79a583424","observation_id":"6258c889-9cb4-4e2d-b62c-dd4fe34b4cd1","resolution":{"observed_at":"2026-08-06T12:47:37.601777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.553511Z","title":"Hlbvh: Hierarchical lbvh construction for real-time ray tracing of dynamic geometry,","venue":null,"work_id":"b1eabe1b-de91-4820-9703-41a9a1270fd6","year":2010},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.804795Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:677661f1bc3596487eed224385a0e626646863e7af737340fcd2dc2ada2c4f73","observation_id":"06958e8c-27bb-49da-9251-15f411de3775","resolution":{"observed_at":"2026-08-06T12:47:37.562691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.511669Z","title":"Nerf: Representing scenes as neural radiance fields for view synthesis,","venue":null,"work_id":"7363c97b-414d-45e2-a196-c162661027ba","year":2021},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.818945Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:801918b5dc941df2130acd2087bcfe56d3e248aeae27c7ec6725839088aaf9c9","observation_id":"ba8082e0-3371-469d-bd7a-2be90643c330","resolution":{"observed_at":"2026-08-06T12:47:37.522192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.480135Z","title":"Rtgs: Enabling real-time gaussian splatting on mobile devices using efficiency-guided pruning and foveated rendering,","venue":null,"work_id":"a844c89a-cdce-43f4-b0a4-f3627bbf8d23","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.831476Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:94c1cc8b7fe4d0c3cc70e85fe6fcf84e2e2b47c48a6cb81aecb55bcfd0ff65e0","observation_id":"1159eafc-3584-4cc4-817b-ffd72c1a6a23","resolution":{"observed_at":"2026-08-06T12:47:37.486875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.436713Z","title":"Gscore: Efficient radiance field rendering via architectural support for 3d gaussian splatting,","venue":null,"work_id":"f33db010-d3e1-429f-a9b2-16f7c9dd369f","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.840955Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:a27d3a131e4126a525976601977c8228d4b4e792183fe9f012225089ca5203bd","observation_id":"7dabec29-361f-4f38-9020-fb41ac5f47b0","resolution":{"observed_at":"2026-08-06T12:47:37.462615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.17898","last_updated":"2024-10-17T07:11:31Z","snapshot_observed_at":"2026-08-07T21:07:59.593997Z","submitted_at":"2024-03-26T17:39:36Z","title":"Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.17898","snapshot_observed_at":"2026-08-06T12:47:35.849275Z","title":"Octree-gs: To- wards consistent real-time rendering with lod-structured 3d gaussians,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.849275Z"},"links":{"cited_paper":"/paper/2403.17898","citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:acec00b9cdd79410d9549cde8cf171870e178f0322bde649950a7f00e08e8c63","observation_id":"5c3017a6-4cca-437c-967c-5fa0402d5e18","resolution":{"observed_at":"2026-08-06T12:47:35.849275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.394786Z","title":"Nvidia jetson orin,","venue":null,"work_id":"16079e33-aa7e-478f-8b18-1f91581c35de","year":2023},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.860276Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:387375cc86d3d73c7f348d439a964aec9df3295f37c94d2a4aa40e09dc09f4c5","observation_id":"468468ae-77f3-4c49-87ef-579116f3986e","resolution":{"observed_at":"2026-08-06T12:47:37.408297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.365047Z","title":"Meta Quest Pro specs,","venue":null,"work_id":"58bdc0fc-c299-448e-9ed4-8f2df6ad86fc","year":2023},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.867581Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:7ec03acdb5ae85e450b15d124df656e4238f62096dbb38db22070d3911743c38","observation_id":"b210a74e-8c8b-4969-b1dc-7b0d1a340f21","resolution":{"observed_at":"2026-08-06T12:47:37.372684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.313060Z","title":"Apple Vision Pro screen refresh rate is up to 100Hz,","venue":null,"work_id":"3d560d42-bf4f-46bc-964b-378e61f60fd5","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.879758Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:8492ce430bf74704c6b20220a5c74e20d0b6965c14744c341a9a0c5b6f979e35","observation_id":"2d009260-764c-400c-ada8-fe976286d973","resolution":{"observed_at":"2026-08-06T12:47:37.323936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.276070Z","title":"Potamoi: Accelerating neural rendering via a unified streaming architecture,","venue":null,"work_id":"366edc50-a6d3-4dae-b553-ee6a12b25282","year":2024},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.889876Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:941e1503770ea6f803ece51166529a6ebf208344b12a214a5413dfb041446636","observation_id":"73f77bbe-a1f8-4992-828d-18c9c172fd98","resolution":{"observed_at":"2026-08-06T12:47:37.284942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.249396Z","title":"Vr-pipe: Streamlining hardware graphics pipeline for volume rendering,","venue":null,"work_id":"6e336855-90c4-4adf-8902-814dc36d3fa3","year":2025},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.899784Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:3e0a4e3b05f1d50e3d10962b45371630de48b1dbca1d207d5b5403247ca54234","observation_id":"0caf2f95-5c8a-46b4-8341-4f1254b0c553","resolution":{"observed_at":"2026-08-06T12:47:37.256545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.206984Z","title":"Uni-render: A unified accelerator for real-time rendering across diverse neural renderers,","venue":null,"work_id":"75cd86a2-3def-4137-a25f-c45f5f76ecdc","year":2025},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.909021Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:453d0533291cb79540deae807a072d7d1b99147da6876245c9f9b1b83bab0646","observation_id":"5ab861c9-fb12-4ad5-bc1a-849a4748cdc0","resolution":{"observed_at":"2026-08-06T12:47:37.216962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.173557Z","title":"Gaussian blending unit: An edge gpu plug-in for real-time gaussian-based rendering in ar/vr,","venue":null,"work_id":"caa40fd0-f075-4268-a905-f3a9e9457e1a","year":2025},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.916471Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:b308c31ad57dff6582987bb40be575ee3582aecc76d2f3468d434db02869f7fc","observation_id":"a5fd3902-a7eb-45f4-aa12-696890c08c5e","resolution":{"observed_at":"2026-08-06T12:47:37.179550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.136274Z","title":"Gsarch: Breaking memory barriers in 3d guassian splatting training via architectural support,","venue":null,"work_id":"c4a61a9b-6bb4-44c9-bc3d-6287b15c51c4","year":2025},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.922332Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:f35221fb9a49e2febf6695415184aed5504d02403f616eb4e72daa354d05f60d","observation_id":"78c3622c-2576-4d70-befb-4f78d8ea2f55","resolution":{"observed_at":"2026-08-06T12:47:37.147864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.083555Z","title":"Crescent: taming memory irregularities for accelerating deep point cloud analytics,","venue":null,"work_id":"133c9866-08a1-49b9-9db5-c40397dff22f","year":2022},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.935561Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:6dca66f3666c3e21096afb16d5c0098efebfd55e688b46685c40799bee866154","observation_id":"9b4db2da-444e-4a29-af6a-0cbedf4415ff","resolution":{"observed_at":"2026-08-06T12:47:37.103988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.058116Z","title":"Tigris: Architecture and algorithms for 3d perception in point clouds,","venue":null,"work_id":"560f1017-eaa2-4d4c-9939-4552ecc3a5fd","year":2019},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.949748Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:be5ce5991675db411582b79a8e26490f13f767bb562b0f62c0f66c8c8561e007","observation_id":"d7bf0587-e605-4c6d-9145-597f8126ecfd","resolution":{"observed_at":"2026-08-06T12:47:37.067978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:37.025200Z","title":"Multidimensional binary search trees used for associative searching,","venue":null,"work_id":"3efe5593-7474-43ed-80fd-fd3eac7b4425","year":1975},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.983131Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:a84d611193f21d8aaf4f060970704794c39f196a6ae085f78523de0d1a03e7a1","observation_id":"cb7a8a4f-2ae9-4e06-8692-b64bcf27a61f","resolution":{"observed_at":"2026-08-06T12:47:37.036632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.982524Z","title":"Geometric modeling using octree encoding,","venue":null,"work_id":"5cf1da88-9cfa-4cf2-bbc6-08cbb9d2aaa7","year":1982},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.989606Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:31483b3bb46a303e71ff839ee32a756a9e7b291e58df243fd4c171a40089bfd1","observation_id":"a6f5cd73-890d-4b9e-9682-9b792c8e594b","resolution":{"observed_at":"2026-08-06T12:47:36.999519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.944039Z","title":"Quicknn: Memory and perfor- mance optimization of kd tree based nearest neighbor search for 3d point clouds,","venue":null,"work_id":"e446019b-d8e5-4a1e-aeb5-6e58401c87ea","year":2020},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:35.999828Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:8f6f4412844ba26d186438d50c281ef7d354f70447804e8399160c5fd7d119a5","observation_id":"b8d2ed4e-a06e-4dcc-b0ef-602ce7cfa49e","resolution":{"observed_at":"2026-08-06T12:47:36.953718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.913123Z","title":"Parallelnn: A parallel octree-based nearest neighbor search accelerator for 3d point clouds,","venue":null,"work_id":"5af80555-8a82-4818-ab26-ff3c057b4818","year":2023},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.012884Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:3673a4727912c14e27820d4816b1d5185339b2769774f7d649686f4152e778e1","observation_id":"c04a33e0-1df0-458a-b111-b0c428e807f2","resolution":{"observed_at":"2026-08-06T12:47:36.920558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.879473Z","title":"Streamgrid: Streaming point cloud analytics via compulsory splitting and deterministic termination,","venue":null,"work_id":"62c5bc28-a4c2-4ba0-b827-8be9d0ecf61c","year":2025},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.026552Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:f8e94ddabeefcc68cab89fd20ef3c0b0221a15e9c128b72f603884dec4934672","observation_id":"b9943e4d-4410-4286-a349-7b770007301d","resolution":{"observed_at":"2026-08-06T12:47:36.895245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.837519Z","title":"Efficient collision detection using bounding volume hierarchies of k- dops,","venue":null,"work_id":"0378fd51-541f-4010-90bc-07ff2fdcdcd2","year":1998},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.032947Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:234804b5be21bf638bf2333b28524cbf4d7a04eb7771b8b3f45f3957e1dc8112","observation_id":"bd7fd40e-d971-4b43-926f-6f1c745adf6b","resolution":{"observed_at":"2026-08-06T12:47:36.854910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.807792Z","title":"Obbtree: A hierarchical structure for rapid interference detection,","venue":null,"work_id":"e1288365-80e4-4257-9835-e47a33698c3f","year":1996},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.041050Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:b891819fb3ade2bb5ce4b551787d3d1f30868ac61763a4fff04a8544a41662d0","observation_id":"e0bb3b56-ad7b-42ab-9d4d-1512c08741f7","resolution":{"observed_at":"2026-08-06T12:47:36.819276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.775003Z","title":"Deepscaletool: A tool for the accurate estimation of technology scaling in the deep-submicron era,","venue":null,"work_id":"0022d66a-6e78-4976-9e8b-91c88c84e3c4","year":2021},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.053627Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:269c8d7d4f1f66ec0c60a630e2e7bc43a57f5a54601004b19980c285b9586bf2","observation_id":"393b7165-aef3-45cf-91d2-0a02190992e9","resolution":{"observed_at":"2026-08-06T12:47:36.785148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.728763Z","title":"Mobile lpddr4 sdram,","venue":null,"work_id":"09986edb-854c-4d2f-904c-8daa2ec470a9","year":2018},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.062218Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:cc5105b156cb531cc60e1df4ffeeb25b6d3c8f17b533de459014876dca92c4b7","observation_id":"2d3451d4-cd08-4c2a-833b-dd272de21ac8","resolution":{"observed_at":"2026-08-06T12:47:36.735948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.695850Z","title":"Micron system power calculators,","venue":null,"work_id":"c053081f-e93b-4c2b-b384-e44c54b1aa16","year":2018},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.072306Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:cac5813255980ae60d2a83350a2a95e58e38e3cb0fc800847a32776e5b1103a8","observation_id":"1547d6b3-deca-47eb-bfd6-f68691fb8bc7","resolution":{"observed_at":"2026-08-06T12:47:36.706416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.668068Z","title":"Tetris: Scalable and efficient neural network acceleration with 3d memory,","venue":null,"work_id":"1f94baf9-3cb4-461c-b7a8-42fde3c49de1","year":2017},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.082334Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:123eb93d78c80cc4f45f098c9a36fb368dac34a3bfdfaf3377da3114b62cc308","observation_id":"8b9cc8a3-33cd-4095-9a13-09e8d2806756","resolution":{"observed_at":"2026-08-06T12:47:36.673766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.621853Z","title":"Ganax: A unified mimd-simd acceleration for generative adversarial networks,","venue":null,"work_id":"defe97a1-b0a9-4a47-ad1a-1100cbe9c9f9","year":2018},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.090583Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:26fb6dfad4e9130e11ad02f30514c994f7c60b3f68019d0f6e5e4786e9f1c6d5","observation_id":"8c016451-e451-4afc-97c3-ef4038f6817a","resolution":{"observed_at":"2026-08-06T12:47:36.638180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.581205Z","title":"Nvidia reveals xavier soc details,","venue":null,"work_id":"f67c43dd-a96a-4b8d-ba1e-f12d19563d18","year":2018},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.097033Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:0414c6f84ef32689cb23bc04ed0a31158cb5e3008f4a519a87c1ee428dbf65ee","observation_id":"57df87be-c372-47f0-922d-b7385164a745","resolution":{"observed_at":"2026-08-06T12:47:36.590839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.553150Z","title":"Apple A15 Die Shot and Annotation - IP Block Area Analysis,","venue":null,"work_id":"1f941bb6-6c2a-47b9-bde6-c0d7a3612bca","year":2021},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.103228Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:a71b4250be0450a50a78747eb8a710008ba7f5406f1983badb1a7e51ab8a95d1","observation_id":"2b7095ca-5a1f-4b0d-a06c-16395ed69648","resolution":{"observed_at":"2026-08-06T12:47:36.563240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.523601Z","title":"Tanks and temples: Benchmarking large-scale scene reconstruction,","venue":null,"work_id":"35dcc0fc-233c-4af3-a0f1-3cf559c1418b","year":2017},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.116963Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:7f5f72be50db056bbae1e46657eb5754af8786450e946e6929ad11a3df46b6eb","observation_id":"1b69f962-0b4e-41d3-9915-643f4ef8e93c","resolution":{"observed_at":"2026-08-06T12:47:36.530730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.487513Z","title":"Deep blending for free-viewpoint image-based rendering,","venue":null,"work_id":"c0a21769-6cc6-49e2-b8c4-154e0d37d6e3","year":2018},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.130644Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:925b6ffa01d5f4745284edd3c521d0532cc8a9a39659bd28acf88ecd36a1a851","observation_id":"7cd9430d-67f7-480d-9f14-ea8f34fc0bd9","resolution":{"observed_at":"2026-08-06T12:47:36.497944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.438997Z","title":"Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps,","venue":null,"work_id":"14d34018-c04a-4d72-949d-0b1c51d90227","year":2023},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.146354Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:4b2d60399089143b6103a39394439d173e832dba7224f96ca3d9803bc67e78e5","observation_id":"e6f31f80-8081-40b8-bd8b-b8ffb3fd717c","resolution":{"observed_at":"2026-08-06T12:47:36.448567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.409833Z","title":"Seele: A unified acceleration framework for real-time gaussian splatting,","venue":null,"work_id":"4037e65e-777f-4a4b-a447-1ba792780af0","year":2025},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.156750Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:6feaa12748e1f64b85c707af8ba5c019b6e424b5f60df29838e2e8ef02cab345","observation_id":"b21775b8-2ba6-410f-b601-e062572132a3","resolution":{"observed_at":"2026-08-06T12:47:36.418364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.371773Z","title":"Lumina: Real-time neural rendering by exploiting computational redundancy,","venue":null,"work_id":"9770a61e-ce7b-4a97-8d0e-0b3dcc46e9de","year":1925},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.168089Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:dbe1f8e31ea52c64ada9515e24d10b5b0f578319ea6b303b28a0ce0a29a54369","observation_id":"8e334f2f-6235-48c0-89b2-236f05a29092","resolution":{"observed_at":"2026-08-06T12:47:36.380198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.343738Z","title":"Streaminggs: V oxel-based streaming 3d gaussian splatting with memory optimization and architectural support,","venue":null,"work_id":"01dd616c-897d-4e75-a24b-7f4237270ba9","year":2025},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.175422Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:dc9e484a78d24f54a3c2a9b06a1f0008fbd69902ec5c08f71e81fe7e42e1a173","observation_id":"d184e6df-e6d6-40bc-b7c0-b1327cd86fbc","resolution":{"observed_at":"2026-08-06T12:47:36.351149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:36.311000Z","title":"Metasapiens: Real-time neural rendering with efficiency-aware pruning and accelerated foveated rendering,","venue":null,"work_id":"bd0bcfaf-26c5-4884-b146-23198d1cc123","year":2025},"citing_paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:36.183936Z"},"links":{"citing_paper":"/paper/2507.21499"},"observation_digest":"sha256:5914167d50d9e40ad82705af6dfd8a145f686593b1c3a7ed6a39b6ddaf99c36a","observation_id":"b2db0714-b082-4c7e-aa83-6a369c8400f5","resolution":{"observed_at":"2026-08-06T12:47:36.321376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.21499","last_updated":"2025-07-29T04:46:48Z","latest_version":1,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-07T21:08:28.903748Z","submitted_at":"2025-07-29T04:46:48Z","title":"SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":53},"total_outbound_references":54},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2507.21499."}