{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:X5AAQ6QLRLEAGH6HZTOFZDZMI4","short_pith_number":"pith:X5AAQ6QL","schema_version":"1.0","canonical_sha256":"bf40087a0b8ac8031fc7ccdc5c8f2c471b3ef438b3f1bfe2bbe07e4f44a3e3a2","source":{"kind":"arxiv","id":"2411.09145","version":4},"attestation_state":"computed","paper":{"title":"Self-Supervised Monocular 4D Scene Reconstruction for Egocentric Videos","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Chengbo Yuan, Geng Chen, Li Yi, Yang Gao","submitted_at":"2024-11-14T02:57:11Z","abstract_excerpt":"Egocentric videos provide valuable insights into human interactions with the physical world, which has sparked growing interest in the computer vision and robotics communities. A critical challenge in fully understanding the geometry and dynamics of egocentric videos is dense scene reconstruction. However, the lack of high-quality labeled datasets in this field has hindered the effectiveness of current supervised learning methods. In this work, we aim to address this issue by exploring an self-supervised dynamic scene reconstruction approach. We introduce EgoMono4D, a novel model that unifies "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.09145","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-14T02:57:11Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"99844295349d64c1e69e325de7473213c26085bbf1b2e9d169907b064199506b","abstract_canon_sha256":"26defcdfded0a1ab4e07a4c55446e14289f5940e79e04e8a51c33e19cf5c8244"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:52.165329Z","signature_b64":"DS4es/nCI9VuvP2/Cd7zI8v9q0r5/JtL+pXqFM9RBYWL5slWeXd03+5ChO63o/j8zvPf/r8Qz8O1zzZ1fE4CBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf40087a0b8ac8031fc7ccdc5c8f2c471b3ef438b3f1bfe2bbe07e4f44a3e3a2","last_reissued_at":"2026-07-05T11:35:52.164898Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:52.164898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Monocular 4D Scene Reconstruction for Egocentric Videos","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Chengbo Yuan, Geng Chen, Li Yi, Yang Gao","submitted_at":"2024-11-14T02:57:11Z","abstract_excerpt":"Egocentric videos provide valuable insights into human interactions with the physical world, which has sparked growing interest in the computer vision and robotics communities. A critical challenge in fully understanding the geometry and dynamics of egocentric videos is dense scene reconstruction. However, the lack of high-quality labeled datasets in this field has hindered the effectiveness of current supervised learning methods. In this work, we aim to address this issue by exploring an self-supervised dynamic scene reconstruction approach. We introduce EgoMono4D, a novel model that unifies "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.09145","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.09145/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.09145","created_at":"2026-07-05T11:35:52.164954+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.09145v4","created_at":"2026-07-05T11:35:52.164954+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09145","created_at":"2026-07-05T11:35:52.164954+00:00"},{"alias_kind":"pith_short_12","alias_value":"X5AAQ6QLRLEA","created_at":"2026-07-05T11:35:52.164954+00:00"},{"alias_kind":"pith_short_16","alias_value":"X5AAQ6QLRLEAGH6H","created_at":"2026-07-05T11:35:52.164954+00:00"},{"alias_kind":"pith_short_8","alias_value":"X5AAQ6QL","created_at":"2026-07-05T11:35:52.164954+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.14025","citing_title":"Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective","ref_index":188,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/X5AAQ6QLRLEAGH6HZTOFZDZMI4","json":"https://pith.science/pith/X5AAQ6QLRLEAGH6HZTOFZDZMI4.json","graph_json":"https://pith.science/api/pith-number/X5AAQ6QLRLEAGH6HZTOFZDZMI4/graph.json","events_json":"https://pith.science/api/pith-number/X5AAQ6QLRLEAGH6HZTOFZDZMI4/events.json","paper":"https://pith.science/paper/X5AAQ6QL"},"agent_actions":{"view_html":"https://pith.science/pith/X5AAQ6QLRLEAGH6HZTOFZDZMI4","download_json":"https://pith.science/pith/X5AAQ6QLRLEAGH6HZTOFZDZMI4.json","view_paper":"https://pith.science/paper/X5AAQ6QL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.09145&json=true","fetch_graph":"https://pith.science/api/pith-number/X5AAQ6QLRLEAGH6HZTOFZDZMI4/graph.json","fetch_events":"https://pith.science/api/pith-number/X5AAQ6QLRLEAGH6HZTOFZDZMI4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X5AAQ6QLRLEAGH6HZTOFZDZMI4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X5AAQ6QLRLEAGH6HZTOFZDZMI4/action/storage_attestation","attest_author":"https://pith.science/pith/X5AAQ6QLRLEAGH6HZTOFZDZMI4/action/author_attestation","sign_citation":"https://pith.science/pith/X5AAQ6QLRLEAGH6HZTOFZDZMI4/action/citation_signature","submit_replication":"https://pith.science/pith/X5AAQ6QLRLEAGH6HZTOFZDZMI4/action/replication_record"}},"created_at":"2026-07-05T11:35:52.164954+00:00","updated_at":"2026-07-05T11:35:52.164954+00:00"}