{"as_of":"2026-08-23T01:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fc7ea2b8bad16657b5dafdd8b3fed27e46487c09ea5828371e2f302278b22c3e","coverage":[{"denominator":63,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":63,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T03:11:15.767649Z","state":"measured"},{"denominator":63,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":63,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2607.28581/citation-record","integrity":"/paper/2607.28581/integrity","json":"/paper/2607.28581/citation-record.json","paper":"/paper/2607.28581"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:09.496673Z","title":"End-to- end object detection with transformers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:09.496673Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:6d297f19d6012d9eabba45e61dff457731f677e532c8942835068471f909f716","observation_id":"f4bc9b23-a3d1-4741-9724-b54097bb2d32","resolution":{"observed_at":"2026-07-31T03:11:09.496673Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:09.569394Z","title":"Efficient geometry-aware 3d generative adversarial networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:09.569394Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:c0fdb465318b321c436a47bad987ab4239ef42562ebb2caef4a5d1f716ee2d36","observation_id":"243725e2-116e-49cf-b6c0-07b571f0c54f","resolution":{"observed_at":"2026-07-31T03:11:09.569394Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:09.640689Z","title":"Dora: Sampling and benchmarking for 3d shape varia- tional auto-encoders","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:09.640689Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:2e621a45c2eda06972724806aa8e858e13b7ec8b3eab50e92d89003dfc4a4913","observation_id":"1e69625b-52cd-4155-9944-77ad387e8e63","resolution":{"observed_at":"2026-07-31T03:11:09.640689Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:09.692172Z","title":"Pra-net: Point relation-aware network for 3d point cloud analysis.IEEE Transactions on Image Processing, 30:4436– 4448, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:09.692172Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:40ebae764b5352c037e56d57602b4ae55d90bb5ef7cdad3415d55ba77eb31c98","observation_id":"8128c4d5-e360-4576-80fd-4e875df22c51","resolution":{"observed_at":"2026-07-31T03:11:09.692172Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:09.775279Z","title":"Objaverse: A universe of annotated 3d objects","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:09.775279Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:d72b2d11fbda2b1e9ee8b4a271f3aa74ea8d2f9fa7df0f02751216bb9a6c1ac2","observation_id":"ad43af98-8d42-4927-9a55-ec4a27ff9fb0","resolution":{"observed_at":"2026-07-31T03:11:09.775279Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:09.819860Z","title":"Scaling recti- fied flow transformers for high-resolution image synthesis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:09.819860Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:3a6caa679ad57c032ec456619a030843a272b08a1fa1f6a49667fb8cbe5ee951","observation_id":"0071e4ef-9880-44cd-ad14-fe57931e7b51","resolution":{"observed_at":"2026-07-31T03:11:09.819860Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.19727","last_updated":"2026-05-19T12:01:23Z","snapshot_observed_at":"2026-08-16T11:00:22.744994Z","submitted_at":"2026-05-19T12:01:23Z","title":"Tango3D: Towards Alignment for Global and Local 2D-3D Correspondence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.19727","snapshot_observed_at":"2026-07-31T03:11:09.938371Z","title":"Tango3d: To- wards alignment for global and local 2d-3d correspondence","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:09.938371Z"},"links":{"cited_paper":"/paper/2605.19727","citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:05a9f7929e7ed260d071afd43a50ba4336010682535a8e1ede7a352546958f41","observation_id":"96258909-e627-4d0a-9471-42a7857268a3","resolution":{"observed_at":"2026-07-31T03:11:09.938371Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:09.985976Z","title":"Efficientdreamer: High-fidelity and robust 3d cre- ation via orthogonal-view diffusion priors","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:09.985976Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:6a9e3fbf5061611cd611b3669a6a583e70df02e00b7a0477f98f36f5ad4675a9","observation_id":"16370aa7-d37c-479f-9e06-ad9cbe18fa88","resolution":{"observed_at":"2026-07-31T03:11:09.985976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15442","last_updated":"2025-06-18T13:14:46Z","snapshot_observed_at":"2026-08-22T03:32:52.591385Z","submitted_at":"2025-06-18T13:14:46Z","title":"Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.15442","snapshot_observed_at":"2026-07-31T03:11:10.094394Z","title":"Hunyuan3d 2.1: From images to high- fidelity 3d assets with production-ready pbr material.arXiv preprint arXiv:2506.15442, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:10.094394Z"},"links":{"cited_paper":"/paper/2506.15442","citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:fdef3dc59d6bed8c6463a9b0e9c7ad0822098804194ad1e7cba4801824f91e4e","observation_id":"627ff360-66d8-44bc-9be3-4a943731b383","resolution":{"observed_at":"2026-07-31T03:11:10.094394Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:10.203843Z","title":"No other representation component is needed: Diffusion transformers can provide representation guidance by themselves","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:10.203843Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:0e1d2a26e41bac35f75e18af8251e87b42ab4ada0957e57e675879e33c186841","observation_id":"9d13b94b-4b53-41d5-a6b6-a62a14fbe975","resolution":{"observed_at":"2026-07-31T03:11:10.203843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02463","last_updated":"2023-05-03T23:59:13Z","snapshot_observed_at":"2026-08-12T18:09:04.196531Z","submitted_at":"2023-05-03T23:59:13Z","title":"Shap-E: Generating Conditional 3D Implicit Functions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02463","snapshot_observed_at":"2026-07-31T03:11:10.264312Z","title":"Shap-e: Generat- ing conditional 3d implicit functions.arXiv preprint arXiv:2305.02463, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:10.264312Z"},"links":{"cited_paper":"/paper/2305.02463","citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:a7f97acb43450fd3c054fc360846774be29c401ca44f3349cc1c2b7eae386bab","observation_id":"7207993f-8f93-4e29-9535-a216baecb52d","resolution":{"observed_at":"2026-07-31T03:11:10.264312Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.16504","last_updated":"2025-06-19T17:57:40Z","snapshot_observed_at":"2026-08-15T17:05:34.457812Z","submitted_at":"2025-06-19T17:57:40Z","title":"Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.16504","snapshot_observed_at":"2026-07-31T03:11:10.360297Z","title":"Hunyuan3d 2.5: Towards high- fidelity 3d assets generation with ultimate details.arXiv preprint arXiv:2506.16504, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:10.360297Z"},"links":{"cited_paper":"/paper/2506.16504","citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:24776b13756c3ed444ec6ff4397c7387affbecfaaa1bb71d7a6d9434e250a832","observation_id":"8f625a36-b9ba-42fe-92c3-ad0e8a228171","resolution":{"observed_at":"2026-07-31T03:11:10.360297Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:10.466591Z","title":"Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:10.466591Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:5755b94f3fc3d481c42e21ecb8abc5e3127b712173152b5823678820dc5654b7","observation_id":"2af96906-3d2d-4cb2-9ae2-7829f81f5421","resolution":{"observed_at":"2026-07-31T03:11:10.466591Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:10.634132Z","title":"Craftsman3d: High-fidelity mesh generation with 3d native diffusion and interactive geometry refiner","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:10.634132Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:a74f853cdbd575a7c1f91eaa464666c5fdb59cf9f81e02f7f5cca170642911f3","observation_id":"4a12de9e-e74a-4d09-99f4-860fb765ba06","resolution":{"observed_at":"2026-07-31T03:11:10.634132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.07747","last_updated":"2025-05-12T16:56:30Z","snapshot_observed_at":"2026-08-19T18:23:10.157981Z","submitted_at":"2025-05-12T16:56:30Z","title":"Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.07747","snapshot_observed_at":"2026-07-31T03:11:10.752901Z","title":"Step1x-3d: Towards high-fidelity and con- trollable generation of textured 3d assets.arXiv preprint arXiv:2505.07747, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:10.752901Z"},"links":{"cited_paper":"/paper/2505.07747","citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:9b8d3c8f53d9b6cb083e959abab98b006141ec087b03c639be6075ea6d06d343","observation_id":"fe53b9c9-4389-4494-8738-7c9b8998fa8f","resolution":{"observed_at":"2026-07-31T03:11:10.752901Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:10.920820Z","title":"Hsgan: Hierarchical graph learning for point cloud generation.IEEE Transactions on Image Processing, 30:4540–4554, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:10.920820Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:6134e1424e6d0db13c5a69e4b8833868092d8e2d326d1dec728ebbecd41f5d91","observation_id":"f168545a-5a59-4139-a046-85fdd4167e13","resolution":{"observed_at":"2026-07-31T03:11:10.920820Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.049386Z","title":"Triposg: High-fidelity 3d shape synthesis using large-scale rectified flow models.IEEE Transactions on Pattern Analysis and Machine Intelligence,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.049386Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:179cbe7990d60fe8177e3c98ad96c7450464f7e3b1cf054647480cd5bbb2e7cc","observation_id":"181185c2-88f6-424c-979b-560712e76f34","resolution":{"observed_at":"2026-07-31T03:11:11.049386Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.120404Z","title":"Pointmamba: A simple state space model for point cloud analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.120404Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:cbe8c182106f83b1a7120d040e23ec6aa6b1ce708fcbbd09e74ef9472a7086ff","observation_id":"5f747e29-1fae-422c-a4e1-1056fdadac01","resolution":{"observed_at":"2026-07-31T03:11:11.120404Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.198360Z","title":"Parameter-efficient fine-tuning in spectral domain for point cloud learning.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.198360Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:fdc39a86e9d7839602dec227bdbe5612dc83de8704cce367c7382a4beb94acf0","observation_id":"511ee524-da60-482e-8af6-e405cbe14476","resolution":{"observed_at":"2026-07-31T03:11:11.198360Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.242675Z","title":"Meta architecture for point cloud analysis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.242675Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:77fdc550a3e4d6e21cfb0fa25aaae637eabaf1c1bf812fc7f416f547ad19d98e","observation_id":"3f11127c-6530-47df-b803-d21687abf392","resolution":{"observed_at":"2026-07-31T03:11:11.242675Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.337895Z","title":"Pufa-gan: A frequency-aware generative adversarial network for 3d point cloud upsampling.IEEE Transactions on Image Processing, 31:7389–7402, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.337895Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:27171b89f4cd27f2aac4d5f53d501864bbbd917f86428374ee605a38c6ed50a7","observation_id":"47883420-d420-4a8d-a26a-fd2a7d1d44e8","resolution":{"observed_at":"2026-07-31T03:11:11.337895Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.374934Z","title":"Openshape: Scaling up 3d shape representation towards open-world understanding","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.374934Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:4d6c3c926fcf22f120ce08704f65d45da44ec5b3f80bcabf94e7abedcc88fcf6","observation_id":"fdad3f96-082c-40e3-b21b-dd99bae7a427","resolution":{"observed_at":"2026-07-31T03:11:11.374934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08751","last_updated":"2022-12-16T23:22:59Z","snapshot_observed_at":"2026-08-17T05:04:12.370590Z","submitted_at":"2022-12-16T23:22:59Z","title":"Point-E: A System for Generating 3D Point Clouds from Complex Prompts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08751","snapshot_observed_at":"2026-07-31T03:11:11.430164Z","title":"Point-e: A system for generat- ing 3d point clouds from complex prompts.arXiv preprint arXiv:2212.08751, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.430164Z"},"links":{"cited_paper":"/paper/2212.08751","citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:2e7cc530c8c07914350b849b5d410b2f1586a3526f9252d7ef56c9ce4fadf91f","observation_id":"939404b7-bf0a-458f-8345-ec7c3493bded","resolution":{"observed_at":"2026-07-31T03:11:11.430164Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.502436Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.502436Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:649148e56e5178386c1b11b4146beeb15b20fa166658566c035fd772aba777a8","observation_id":"7cad2e6e-55de-4a2e-88e9-bee72d872c50","resolution":{"observed_at":"2026-07-31T03:11:11.502436Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.589862Z","title":"Masked autoencoders for point cloud self-supervised learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.589862Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:8fc576c8ae7dbf2a63a4cd67119bf76e7f89affcb89e58276b2ada045987b073","observation_id":"90f7e4b2-77ee-47fb-b419-9d25d52661e8","resolution":{"observed_at":"2026-07-31T03:11:11.589862Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.667893Z","title":"Dreamfusion: Text-to-3d using 2d diffusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.667893Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:efa85546e40996938e96d141124bf01ecdfb865ec981c231850f7fdf393d9701","observation_id":"1988f5ea-e35c-48e5-adf7-81aa2566cfa9","resolution":{"observed_at":"2026-07-31T03:11:11.667893Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.762583Z","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.762583Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:ff9680ff4349343b22bba24a0ceb55d5ca38e4741a4ba9d779eae1ef89e935ca","observation_id":"d8ff291c-6026-4f7a-bf49-f3086413e9eb","resolution":{"observed_at":"2026-07-31T03:11:11.762583Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.871860Z","title":"Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.871860Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:b4d8b7f691ed857574722805f933b3189968a49de8bf1c9356f1e70828dd2827","observation_id":"d42e7a2e-98db-4fc4-a160-4f782caf15ce","resolution":{"observed_at":"2026-07-31T03:11:11.871860Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:11.977569Z","title":"Shapellm: Universal 3d object understanding for embodied interaction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:11.977569Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:a98720e294bb861eb985fd63419d8221e80c5a52d88b1712cd8c03e7ffbeafbd","observation_id":"f712fd36-152d-4d4b-b94a-365a96a9c271","resolution":{"observed_at":"2026-07-31T03:11:11.977569Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:12.054362Z","title":"Pointnext: Revisiting pointnet++ with improved training and scaling strategies.Proc","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.054362Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:cac99b138172091d05d8d7ae476e24d514cd01a1cadf9bd15c38e15ad4f34375","observation_id":"d4df6eee-85cb-444c-803a-03c52ff76657","resolution":{"observed_at":"2026-07-31T03:11:12.054362Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:12.120752Z","title":"Global-local bidi- rectional reasoning for unsupervised representation learning of 3d point clouds","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.120752Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:14a8813bec440e56d75fb8d9401217c63aa018ddc4b83211583d422ea0b85f68","observation_id":"06f5d716-1b5d-4e72-ae55-d71b34162ce0","resolution":{"observed_at":"2026-07-31T03:11:12.120752Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:12.176834Z","title":"Mvdream: Multi-view diffusion for 3d gen- eration","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.176834Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:b75e05e97478cfef9d32f9a1d0bce96cdb74eba5df301a4f9d8dd7ead48f74af","observation_id":"8e7d193f-c0c8-43b8-b59d-7ab5cb53041c","resolution":{"observed_at":"2026-07-31T03:11:12.176834Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:12.256248Z","title":"3d neural field generation using triplane diffusion","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.256248Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:6b285581f428ba815efb5c4c6b2d9aa91c3020335a3c3d74f7c54f2752aff9eb","observation_id":"1e74d7d7-06f4-44e2-b718-b6726cef67c8","resolution":{"observed_at":"2026-07-31T03:11:12.256248Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:12.396335Z","title":"Meshgpt: Generating triangle meshes with decoder-only transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.396335Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:1c80b0a34fbd02de27c3f0b5dbdad6e038b5a67daf73d41e6109256e746bf13c","observation_id":"0bb6c6c6-a536-47fd-8b4e-d4794d6a94ad","resolution":{"observed_at":"2026-07-31T03:11:12.396335Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:12.503265Z","title":"What mat- ters for representation alignment: Global information or spa- tial structure? InProc","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.503265Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:80b7bdb15c62665db139128710e96c5238e5562b497f8c34a9451a15d9cf1b01","observation_id":"cd312a6e-3988-4222-ac5b-a58b46c325b5","resolution":{"observed_at":"2026-07-31T03:11:12.503265Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:12.607231Z","title":"X-3d: Explicit 3d structure modeling for point cloud recog- nition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.607231Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:91052cbfa2d97055b5c91d896312196e16a9252aa83f887aa30ce373b21ed011","observation_id":"7cc6ecea-2e5e-4da1-92fa-08053ca4897f","resolution":{"observed_at":"2026-07-31T03:11:12.607231Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:12.726289Z","title":"U-repa: Aligning diffu- sion u-nets to vits.arXiv preprint arXiv:2503.18414, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.726289Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:8980ef4a65ef7dcae20eab21f6b9037c3bb6ff3a779971c319723093a8db5da7","observation_id":"315099a9-3283-4acc-926e-def3c04d2652","resolution":{"observed_at":"2026-07-31T03:11:12.726289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02151","last_updated":"2024-03-04T16:00:56Z","snapshot_observed_at":"2026-07-06T17:39:16.540553Z","submitted_at":"2024-03-04T16:00:56Z","title":"TripoSR: Fast 3D Object Reconstruction from a Single Image","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02151","snapshot_observed_at":"2026-07-31T03:11:12.859071Z","title":"Triposr: Fast 3d object reconstruction from a single image.arXiv preprint arXiv:2403.02151, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.859071Z"},"links":{"cited_paper":"/paper/2403.02151","citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:941afb40a206fceb306902cf3038c635586a1696cb94d2a122507826d9cbf6d4","observation_id":"b816a8cd-4aac-4527-afe9-f24affda0c74","resolution":{"observed_at":"2026-07-31T03:11:12.859071Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:12.979821Z","title":"Lion: Latent point dif- fusion models for 3d shape generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:12.979821Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:24189ec2ec67b6b5da4d0eb1551a7fb466462931b2767b6adc252d890a610c32","observation_id":"e730d741-5d7e-4832-8b63-f6537df78446","resolution":{"observed_at":"2026-07-31T03:11:12.979821Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:13.126607Z","title":"Cad: Photorealistic 3d generation via adversarial distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:13.126607Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:02e56c900d264595ba860fe681b38742547085e665d65cdb4d8f6927547a9e2a","observation_id":"bafded51-9938-4c4a-b24c-d89f313e4105","resolution":{"observed_at":"2026-07-31T03:11:13.126607Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:13.235088Z","title":"Gpsformer: A global perception and local struc- ture fitting-based transformer for point cloud understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:13.235088Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:ceac6254d263d907fb87cfd93889c0651563cb5207ba70254777fec66503dbab","observation_id":"093789e1-b45f-481f-9f2f-b4ccb26f7820","resolution":{"observed_at":"2026-07-31T03:11:13.235088Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:13.310343Z","title":"Rethinking masked representation learning for 3d point cloud understanding.IEEE Transactions on Im- age Processing, 34:247–262, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:13.310343Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:2db4e28abd9bbc305e282628ce2a2ad372e05ba26479bb69e0503ef076182e0a","observation_id":"87f14773-2805-43b9-bc2a-fa97746ad643","resolution":{"observed_at":"2026-07-31T03:11:13.310343Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:13.395646Z","title":"Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:13.395646Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:6af6fed18aa362953c5792a157fa5dbfc84fcb7ff193d631eec10072729c2537","observation_id":"1db4c2a1-0dd7-4b07-82b9-bb457d464977","resolution":{"observed_at":"2026-07-31T03:11:13.395646Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:13.531033Z","title":"Vggt: Vi- sual geometry grounded transformer","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:13.531033Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:cc5f87c690ba837120d40e77ad80898ee1863d5a33d64d324fafea6d86b1f97b","observation_id":"32c96b76-45fe-4de7-bda9-489447d08c1a","resolution":{"observed_at":"2026-07-31T03:11:13.531033Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:13.727567Z","title":"Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:13.727567Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:7d95b2bc4a982f2fa0bdf52872fe8c76566268068ce8f0f30c96267b368b4da4","observation_id":"80973a0a-ccea-4655-a1cd-01b96b570ebf","resolution":{"observed_at":"2026-07-31T03:11:13.727567Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:13.861753Z","title":"Representation entanglement for genera- tion: Training diffusion transformers is much easier than you think","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:13.861753Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:48e58ec4109425226041f5150b2a54160e98defcca178e0d47a65b2c72446e01","observation_id":"328d9fec-2dae-4597-82b5-f8b0e11743f2","resolution":{"observed_at":"2026-07-31T03:11:13.861753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.07982","last_updated":"2026-05-05T14:55:01Z","snapshot_observed_at":"2026-08-14T21:13:23.101760Z","submitted_at":"2025-07-10T17:55:08Z","title":"Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.07982","snapshot_observed_at":"2026-07-31T03:11:14.056390Z","title":"Geometry forcing: Marrying video diffusion and 3d representation for consistent world modeling.arXiv preprint arXiv:2507.07982, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:14.056390Z"},"links":{"cited_paper":"/paper/2507.07982","citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:b0bd186da6ba8a35ee02f3f127cf09dee9354f533070ade67abbdaa24b0d75f4","observation_id":"ed2801e8-eb09-4515-baf2-64145eecb051","resolution":{"observed_at":"2026-07-31T03:11:14.056390Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:14.235914Z","title":"Direct3d-s2: Gigascale 3d generation made easy with spatial sparse attention","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:14.235914Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:1cdc85629bd1f0526b0ac288d5ccc0f8e52c796f97250259546facf1eacce72f","observation_id":"c7bca9cb-3d34-4174-a2fc-8f4029c89555","resolution":{"observed_at":"2026-07-31T03:11:14.235914Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:14.394398Z","title":"Point transformer v2: Grouped vector at- tention and partition-based pooling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:14.394398Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:71cb55a5cce37778d33a820e942cbf60cefec8aa470f499ee11f76d81e8553b3","observation_id":"93534359-e24c-48ea-a993-7a6563414e93","resolution":{"observed_at":"2026-07-31T03:11:14.394398Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:14.541979Z","title":"Point transformer v3: Simpler faster stronger","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:14.541979Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:b4451e5b02d52e7910e279ac834e4a0af8062252b789beeee0ac912173a110d6","observation_id":"f753728f-1ac7-4ad6-88da-07ec3f7246b3","resolution":{"observed_at":"2026-07-31T03:11:14.541979Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:14.604312Z","title":"Structured 3d latents for scalable and versatile 3d gen- eration","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:14.604312Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:bdefe44f8188bf7a3c1e1ebd2cc18201215af7c370946746e7c3edc50d10f69d","observation_id":"71d980f9-8da7-4f50-89fb-89f87e4d2fda","resolution":{"observed_at":"2026-07-31T03:11:14.604312Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:14.763745Z","title":"Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:14.763745Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:80455db6502099dcde17e3f799740c69acb2e081aa0ba490c5212cfd119809f8","observation_id":"e2cc0a3f-2612-4c94-97aa-66769cace9a6","resolution":{"observed_at":"2026-07-31T03:11:14.763745Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:14.866551Z","title":"Ulip-2: Towards scal- able multimodal pre-training for 3d understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:14.866551Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:333010133dee26161b6f086212dd4a75b71fc761cf3e9c75dc90e3aa6e636788","observation_id":"1cf565f3-90ce-459f-beb3-1e1ff2d9f8c9","resolution":{"observed_at":"2026-07-31T03:11:14.866551Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:14.971134Z","title":"Single- view 3d mesh reconstruction for seen and unseen categories","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:14.971134Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:84b699494a305178de46ebbb172e57fe79c4066bf008a99ddfc53fea84686d0d","observation_id":"ba4611a3-26f5-45b2-8dca-98c7d96bb8b1","resolution":{"observed_at":"2026-07-31T03:11:14.971134Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:15.038820Z","title":"Hi3dgen: High-fidelity 3d geometry generation from images via normal bridging","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:15.038820Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:44e4bbd725bff76c3768b8af03ce75292e60948fce038de3bc468c3ee77adeb4","observation_id":"b22399ca-d5db-48e4-85ad-a85c387bc07c","resolution":{"observed_at":"2026-07-31T03:11:15.038820Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:15.092179Z","title":"Homugan: A 3d-aware gan with the method of cylindrical spatial-constrained sampling.IEEE Transactions on Image Processing, 34:320–334, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:15.092179Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:7166e5f830a385026ce6bbdf7994dddb1ae88d731741f1cb25d253c872faea3a","observation_id":"86226d67-db45-4576-89e7-577d40bf87c2","resolution":{"observed_at":"2026-07-31T03:11:15.092179Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:15.190903Z","title":"Representation alignment for generation: Training diffusion transformers is easier than you think","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:15.190903Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:9a29ca4a5f62bbc2e1d8179e96153081eae7f7dd659f6a4a2139b60f34019174","observation_id":"f4d9c050-0a0d-4156-b4d5-c8d829c83bb1","resolution":{"observed_at":"2026-07-31T03:11:15.190903Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:15.328354Z","title":"Pointclip: Point cloud understanding by clip","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:15.328354Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:8de63e05209581a544cfae01d3566d417ea8dc47c7cf08cd5c875ea596784bc1","observation_id":"9b8f2c95-62c8-4b66-abc5-575342e455c5","resolution":{"observed_at":"2026-07-31T03:11:15.328354Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:15.395982Z","title":"Hypergraph spectral analysis and processing in 3d point cloud.IEEE Transactions on Image Processing, 30:1193–1206, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:15.395982Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:71735c78fbdd11562bfd75aaf60830144d0d9f1c343157677f4375bb778878a0","observation_id":"3cebf950-79f6-450a-9e67-f5bceb1ee296","resolution":{"observed_at":"2026-07-31T03:11:15.395982Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:15.549114Z","title":"Vide- orepa: Learning physics for video generation through rela- tional alignment with foundation models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:15.549114Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:dec11638ea09d5282f04b138614203df78756aee9b5ad963986042ee71cb03a8","observation_id":"df4f8b77-4834-4f0b-bf41-66a20757a833","resolution":{"observed_at":"2026-07-31T03:11:15.549114Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:15.635219Z","title":"Point transformer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:15.635219Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:505aad101fc0640a4ae76260ceb8ca6018d2a00c2530292afc2359f39343abfb","observation_id":"a1629ba9-9c2a-433f-aa12-524b181ff116","resolution":{"observed_at":"2026-07-31T03:11:15.635219Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:15.680284Z","title":"Uni3d: Exploring unified 3d representation at scale","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:15.680284Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:a3a470cbdd662931af428d66ccd145994be828bb28f9ad730dc1e79acaa1fe3a","observation_id":"74a889fe-a8cf-4783-a5ee-6931c4696ba6","resolution":{"observed_at":"2026-07-31T03:11:15.680284Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T03:11:15.767649Z","title":"Recur- rent diffusion for 3d point cloud generation from a single image.IEEE Transactions on Image Processing, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-07-31T03:11:15.767649Z"},"links":{"citing_paper":"/paper/2607.28581"},"observation_digest":"sha256:f347b3a38c423c429dd2b937052a73ba0b5b8374880c57aeabd3bfc7e52f200b","observation_id":"f3147d8b-a000-4bf3-bb46-f6bbc1a425a4","resolution":{"observed_at":"2026-07-31T03:11:15.767649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.28581","last_updated":"2026-07-30T17:40:08Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T09:38:51.788245Z","submitted_at":"2026-07-30T17:40:08Z","title":"ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation"},"reference_resolution":{"displayed":63,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":63,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":63},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2607.28581."}