{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ZIWHXRAR3RQ2OYY3BGWVQCD7US","short_pith_number":"pith:ZIWHXRAR","canonical_record":{"source":{"id":"2604.22482","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-24T12:03:27Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"6bfcc7e5ef0ec06ff91b560db6db37795ff17b6fe53ffe7523e757906131619f","abstract_canon_sha256":"9fcc353c105a086da66954c8bb071ea66679acf03f929453d3fa9b30d38c2af7"},"schema_version":"1.0"},"canonical_sha256":"ca2c7bc411dc61a7631b09ad58087fa49da69f8e4eb2470c191bb875e5b44924","source":{"kind":"arxiv","id":"2604.22482","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.22482","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"arxiv_version","alias_value":"2604.22482v2","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.22482","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"pith_short_12","alias_value":"ZIWHXRAR3RQ2","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"pith_short_16","alias_value":"ZIWHXRAR3RQ2OYY3","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"pith_short_8","alias_value":"ZIWHXRAR","created_at":"2026-06-09T02:07:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ZIWHXRAR3RQ2OYY3BGWVQCD7US","target":"record","payload":{"canonical_record":{"source":{"id":"2604.22482","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-24T12:03:27Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"6bfcc7e5ef0ec06ff91b560db6db37795ff17b6fe53ffe7523e757906131619f","abstract_canon_sha256":"9fcc353c105a086da66954c8bb071ea66679acf03f929453d3fa9b30d38c2af7"},"schema_version":"1.0"},"canonical_sha256":"ca2c7bc411dc61a7631b09ad58087fa49da69f8e4eb2470c191bb875e5b44924","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:26.799110Z","signature_b64":"BqOdZQkazSlw3JWXEeJcghooDtEGcCYJbkTMF64aHWA2v2FFafxvD+gMnuknWCasuxxQVAIxhnhvo+LBxGBNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca2c7bc411dc61a7631b09ad58087fa49da69f8e4eb2470c191bb875e5b44924","last_reissued_at":"2026-06-09T02:07:26.798260Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:26.798260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.22482","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T02:07:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pwqQIzyI1opFEm8THMSfxJSBhRw20Txak6I2fA+Ze11wxZzkDLpsbyMwzzJgTybLRV9AzhQMMxSTcwVeOsL1BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T20:49:53.160194Z"},"content_sha256":"78d3b81c4d25710d2180950bcfa5e7db20ded86bbaddd6e8b44929105c8800bd","schema_version":"1.0","event_id":"sha256:78d3b81c4d25710d2180950bcfa5e7db20ded86bbaddd6e8b44929105c8800bd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ZIWHXRAR3RQ2OYY3BGWVQCD7US","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Holo360D: A Large-Scale Real-World Dataset with Continuous Trajectories for Advancing Panoramic 3D Reconstruction and Beyond","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"Holo360D supplies the first large-scale dataset of continuous panoramic sequences with aligned high-completeness depth maps.","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Hui Xiong, Jing Ou, Jinjing Zhu, Shuai Zhang, Tongyan Hua, Wufan Zhao, Yinrui Ren, Zhuoxiao Li, Zidong Cao","submitted_at":"2026-04-24T12:03:27Z","abstract_excerpt":"While feed-forward 3D reconstruction models have advanced rapidly, they still exhibit degraded performance on panoramas due to spherical distortions. Moreover, existing panoramic 3D datasets are predominantly collected with 360 cameras fixed at discrete locations, resulting in discontinuous trajectories. These limitations critically hinder the development of panoramic feed-forward 3D reconstruction, especially for the multi-view setting. In this paper, we present Holo360D, a comprehensive dataset containing 109,495 panoramas paired with registered point clouds, meshes, and aligned camera poses"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"To our knowledge, Holo360D is the first large-scale dataset that provides continuous panoramic sequences with accurately aligned high-completeness depth maps. Our results demonstrate that Holo360D delivers superior training signals and provides a comprehensive benchmark for advancing panoramic 3D reconstruction models.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The post-processing pipeline (geometry denoising, mesh hole filling, region-specific remeshing) combined with online and offline SLAM produces sufficiently accurate alignments and high-completeness depth maps without introducing significant artifacts or biases that would limit model training effectiveness.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Holo360D is the first large-scale dataset providing continuous panoramic sequences with accurately aligned high-completeness depth maps and meshes for training panoramic 3D reconstruction models.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Holo360D supplies the first large-scale dataset of continuous panoramic sequences with aligned high-completeness depth maps.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"aa6cbe61f1136704c205ab1cb9e853b718ba9cc959dba680752627e69e91aad1"},"source":{"id":"2604.22482","kind":"arxiv","version":2},"verdict":{"id":"f3f65096-9481-4b8c-b28a-fab071372804","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T12:26:46.147137Z","strongest_claim":"To our knowledge, Holo360D is the first large-scale dataset that provides continuous panoramic sequences with accurately aligned high-completeness depth maps. Our results demonstrate that Holo360D delivers superior training signals and provides a comprehensive benchmark for advancing panoramic 3D reconstruction models.","one_line_summary":"Holo360D is the first large-scale dataset providing continuous panoramic sequences with accurately aligned high-completeness depth maps and meshes for training panoramic 3D reconstruction models.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The post-processing pipeline (geometry denoising, mesh hole filling, region-specific remeshing) combined with online and offline SLAM produces sufficiently accurate alignments and high-completeness depth maps without introducing significant artifacts or biases that would limit model training effectiveness.","pith_extraction_headline":"Holo360D supplies the first large-scale dataset of continuous panoramic sequences with aligned high-completeness depth maps."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.22482/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T10:39:48.506953Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T23:56:36.592127Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"667f04717cb0c5347c2d73fbb28959818c7803d73c300552bfad463b91bcf8b8"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"f3f65096-9481-4b8c-b28a-fab071372804"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T02:07:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zscsDcgTCvCOVFujHGgGa5WnjsKykr5oF5KR5UKTjUuLMT1o3ESHM2Sf8xh62CTzkR1/86zhe0dgnIH6okRWAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T20:49:53.160714Z"},"content_sha256":"73bc75c70fe39dd1e6c7bfca2f05e950e988149682990853490f045c4454dc1f","schema_version":"1.0","event_id":"sha256:73bc75c70fe39dd1e6c7bfca2f05e950e988149682990853490f045c4454dc1f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZIWHXRAR3RQ2OYY3BGWVQCD7US/bundle.json","state_url":"https://pith.science/pith/ZIWHXRAR3RQ2OYY3BGWVQCD7US/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZIWHXRAR3RQ2OYY3BGWVQCD7US/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-10T20:49:53Z","links":{"resolver":"https://pith.science/pith/ZIWHXRAR3RQ2OYY3BGWVQCD7US","bundle":"https://pith.science/pith/ZIWHXRAR3RQ2OYY3BGWVQCD7US/bundle.json","state":"https://pith.science/pith/ZIWHXRAR3RQ2OYY3BGWVQCD7US/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZIWHXRAR3RQ2OYY3BGWVQCD7US/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZIWHXRAR3RQ2OYY3BGWVQCD7US","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9fcc353c105a086da66954c8bb071ea66679acf03f929453d3fa9b30d38c2af7","cross_cats_sorted":["cs.GR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-24T12:03:27Z","title_canon_sha256":"6bfcc7e5ef0ec06ff91b560db6db37795ff17b6fe53ffe7523e757906131619f"},"schema_version":"1.0","source":{"id":"2604.22482","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.22482","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"arxiv_version","alias_value":"2604.22482v2","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.22482","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"pith_short_12","alias_value":"ZIWHXRAR3RQ2","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"pith_short_16","alias_value":"ZIWHXRAR3RQ2OYY3","created_at":"2026-06-09T02:07:26Z"},{"alias_kind":"pith_short_8","alias_value":"ZIWHXRAR","created_at":"2026-06-09T02:07:26Z"}],"graph_snapshots":[{"event_id":"sha256:73bc75c70fe39dd1e6c7bfca2f05e950e988149682990853490f045c4454dc1f","target":"graph","created_at":"2026-06-09T02:07:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"To our knowledge, Holo360D is the first large-scale dataset that provides continuous panoramic sequences with accurately aligned high-completeness depth maps. Our results demonstrate that Holo360D delivers superior training signals and provides a comprehensive benchmark for advancing panoramic 3D reconstruction models."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The post-processing pipeline (geometry denoising, mesh hole filling, region-specific remeshing) combined with online and offline SLAM produces sufficiently accurate alignments and high-completeness depth maps without introducing significant artifacts or biases that would limit model training effectiveness."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Holo360D is the first large-scale dataset providing continuous panoramic sequences with accurately aligned high-completeness depth maps and meshes for training panoramic 3D reconstruction models."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Holo360D supplies the first large-scale dataset of continuous panoramic sequences with aligned high-completeness depth maps."}],"snapshot_sha256":"aa6cbe61f1136704c205ab1cb9e853b718ba9cc959dba680752627e69e91aad1"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-21T10:39:48.506953Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T23:56:36.592127Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.22482/integrity.json","findings":[],"snapshot_sha256":"667f04717cb0c5347c2d73fbb28959818c7803d73c300552bfad463b91bcf8b8","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While feed-forward 3D reconstruction models have advanced rapidly, they still exhibit degraded performance on panoramas due to spherical distortions. Moreover, existing panoramic 3D datasets are predominantly collected with 360 cameras fixed at discrete locations, resulting in discontinuous trajectories. These limitations critically hinder the development of panoramic feed-forward 3D reconstruction, especially for the multi-view setting. In this paper, we present Holo360D, a comprehensive dataset containing 109,495 panoramas paired with registered point clouds, meshes, and aligned camera poses","authors_text":"Hui Xiong, Jing Ou, Jinjing Zhu, Shuai Zhang, Tongyan Hua, Wufan Zhao, Yinrui Ren, Zhuoxiao Li, Zidong Cao","cross_cats":["cs.GR"],"headline":"Holo360D supplies the first large-scale dataset of continuous panoramic sequences with aligned high-completeness depth maps.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-24T12:03:27Z","title":"Holo360D: A Large-Scale Real-World Dataset with Continuous Trajectories for Advancing Panoramic 3D Reconstruction and Beyond"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.22482","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-08T12:26:46.147137Z","id":"f3f65096-9481-4b8c-b28a-fab071372804","model_set":{"reader":"grok-4.3"},"one_line_summary":"Holo360D is the first large-scale dataset providing continuous panoramic sequences with accurately aligned high-completeness depth maps and meshes for training panoramic 3D reconstruction models.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Holo360D supplies the first large-scale dataset of continuous panoramic sequences with aligned high-completeness depth maps.","strongest_claim":"To our knowledge, Holo360D is the first large-scale dataset that provides continuous panoramic sequences with accurately aligned high-completeness depth maps. Our results demonstrate that Holo360D delivers superior training signals and provides a comprehensive benchmark for advancing panoramic 3D reconstruction models.","weakest_assumption":"The post-processing pipeline (geometry denoising, mesh hole filling, region-specific remeshing) combined with online and offline SLAM produces sufficiently accurate alignments and high-completeness depth maps without introducing significant artifacts or biases that would limit model training effectiveness."}},"verdict_id":"f3f65096-9481-4b8c-b28a-fab071372804"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:78d3b81c4d25710d2180950bcfa5e7db20ded86bbaddd6e8b44929105c8800bd","target":"record","created_at":"2026-06-09T02:07:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9fcc353c105a086da66954c8bb071ea66679acf03f929453d3fa9b30d38c2af7","cross_cats_sorted":["cs.GR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-24T12:03:27Z","title_canon_sha256":"6bfcc7e5ef0ec06ff91b560db6db37795ff17b6fe53ffe7523e757906131619f"},"schema_version":"1.0","source":{"id":"2604.22482","kind":"arxiv","version":2}},"canonical_sha256":"ca2c7bc411dc61a7631b09ad58087fa49da69f8e4eb2470c191bb875e5b44924","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ca2c7bc411dc61a7631b09ad58087fa49da69f8e4eb2470c191bb875e5b44924","first_computed_at":"2026-06-09T02:07:26.798260Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T02:07:26.798260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BqOdZQkazSlw3JWXEeJcghooDtEGcCYJbkTMF64aHWA2v2FFafxvD+gMnuknWCasuxxQVAIxhnhvo+LBxGBNBw==","signature_status":"signed_v1","signed_at":"2026-06-09T02:07:26.799110Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.22482","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:78d3b81c4d25710d2180950bcfa5e7db20ded86bbaddd6e8b44929105c8800bd","sha256:73bc75c70fe39dd1e6c7bfca2f05e950e988149682990853490f045c4454dc1f"],"state_sha256":"727bd3459e4de55902f1573b73d7081256a5835c6821c6b91ad577acb3dd2296"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mrgx6Jr0eOiRE1kBrTDaELjAVs6wpwSj2GvlSFqGssxysB4Hp4fc253kUDOTx8eln0HLDDgvWAQLY8G697pbBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T20:49:53.163019Z","bundle_sha256":"79fa24105f883bc89aac4bbd4c86ff134311782dce8022e085e5aa836d6455cb"}}