{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:Q44KAUX5HWX4YXYZEMX2B6HTFJ","short_pith_number":"pith:Q44KAUX5","schema_version":"1.0","canonical_sha256":"8738a052fd3dafcc5f19232fa0f8f32a75c9be429fee7916f82df1329ac6e144","source":{"kind":"arxiv","id":"2607.18153","version":1},"attestation_state":"computed","paper":{"title":"Robust Multimodal Dynamic Object Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guoquan Huang, Hanzhi Chang, Penghui Huang, Yinian Mao, Zhe Xin","submitted_at":"2026-07-20T16:51:48Z","abstract_excerpt":"Dynamic object segmentation plays a critical role in many visual applications such as static scene reconstruction from dynamic videos. However, existing optical flow-based methods fail to ensure consistent static/dynamic segmentation along object boundaries, while 3D reconstruction-based approaches are highly sensitive to reconstruction errors. To address these limitations, we present a dynamic object segmentation framework that can generate both precise and complete dynamic masks by integrating multimodal cues including 2D point tracks, 3D reconstruction, and semantic information. We design a"},"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":"2607.18153","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T16:51:48Z","cross_cats_sorted":[],"title_canon_sha256":"008ab08fffd0634fc5dc0bb6b059430d7d0c9ac978cc3db6aa3ba8e1e6d89912","abstract_canon_sha256":"ed834ebe5bf8a1e79c7bce86bb9bff46817174ccde3346120b706336c992d704"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T02:22:19.402163Z","signature_b64":"UeAEhQv2sS3KrGUFrm29iyPFQEkXguLc6ANLWQbVKWwdP70RS7aSw4xlUCHcVlpq/PHP66x3wK1EBAYagpI/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8738a052fd3dafcc5f19232fa0f8f32a75c9be429fee7916f82df1329ac6e144","last_reissued_at":"2026-07-21T02:22:19.401336Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T02:22:19.401336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Multimodal Dynamic Object Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guoquan Huang, Hanzhi Chang, Penghui Huang, Yinian Mao, Zhe Xin","submitted_at":"2026-07-20T16:51:48Z","abstract_excerpt":"Dynamic object segmentation plays a critical role in many visual applications such as static scene reconstruction from dynamic videos. However, existing optical flow-based methods fail to ensure consistent static/dynamic segmentation along object boundaries, while 3D reconstruction-based approaches are highly sensitive to reconstruction errors. To address these limitations, we present a dynamic object segmentation framework that can generate both precise and complete dynamic masks by integrating multimodal cues including 2D point tracks, 3D reconstruction, and semantic information. We design a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18153","kind":"arxiv","version":1},"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/2607.18153/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":"2607.18153","created_at":"2026-07-21T02:22:19.401767+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.18153v1","created_at":"2026-07-21T02:22:19.401767+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18153","created_at":"2026-07-21T02:22:19.401767+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q44KAUX5HWX4","created_at":"2026-07-21T02:22:19.401767+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q44KAUX5HWX4YXYZ","created_at":"2026-07-21T02:22:19.401767+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q44KAUX5","created_at":"2026-07-21T02:22:19.401767+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q44KAUX5HWX4YXYZEMX2B6HTFJ","json":"https://pith.science/pith/Q44KAUX5HWX4YXYZEMX2B6HTFJ.json","graph_json":"https://pith.science/api/pith-number/Q44KAUX5HWX4YXYZEMX2B6HTFJ/graph.json","events_json":"https://pith.science/api/pith-number/Q44KAUX5HWX4YXYZEMX2B6HTFJ/events.json","paper":"https://pith.science/paper/Q44KAUX5"},"agent_actions":{"view_html":"https://pith.science/pith/Q44KAUX5HWX4YXYZEMX2B6HTFJ","download_json":"https://pith.science/pith/Q44KAUX5HWX4YXYZEMX2B6HTFJ.json","view_paper":"https://pith.science/paper/Q44KAUX5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.18153&json=true","fetch_graph":"https://pith.science/api/pith-number/Q44KAUX5HWX4YXYZEMX2B6HTFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/Q44KAUX5HWX4YXYZEMX2B6HTFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q44KAUX5HWX4YXYZEMX2B6HTFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q44KAUX5HWX4YXYZEMX2B6HTFJ/action/storage_attestation","attest_author":"https://pith.science/pith/Q44KAUX5HWX4YXYZEMX2B6HTFJ/action/author_attestation","sign_citation":"https://pith.science/pith/Q44KAUX5HWX4YXYZEMX2B6HTFJ/action/citation_signature","submit_replication":"https://pith.science/pith/Q44KAUX5HWX4YXYZEMX2B6HTFJ/action/replication_record"}},"created_at":"2026-07-21T02:22:19.401767+00:00","updated_at":"2026-07-21T02:22:19.401767+00:00"}