{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5MGVME6DZTD35V5KYMD3UJ4YEF","short_pith_number":"pith:5MGVME6D","schema_version":"1.0","canonical_sha256":"eb0d5613c3ccc7bed7aac307ba2798217047d867328e469dd23ff43e3baac972","source":{"kind":"arxiv","id":"2412.10972","version":2},"attestation_state":"computed","paper":{"title":"DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"David Rozenberszki, Luca Wiehe, Luis Wiedmann","submitted_at":"2024-12-14T21:26:44Z","abstract_excerpt":"Open-set 3D segmentation represents a major point of interest for multiple downstream robotics and augmented/virtual reality applications. We present a decoupled 3D segmentation pipeline to ensure modularity and adaptability to novel 3D representations as well as semantic segmentation foundation models. We first reconstruct a scene with 3D Gaussians and learn class-agnostic features through contrastive supervision from a 2D instance proposal network. These 3D features are then clustered to form coarse object- or part-level masks. Finally, we match each 3D cluster to class-aware masks predicted"},"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":"2412.10972","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-14T21:26:44Z","cross_cats_sorted":[],"title_canon_sha256":"1efed46e0e591df3fd7c999b3ef7dacb2b2a8fdd93b1af5c03ef50b55f3edf81","abstract_canon_sha256":"f1cb648f1cfa3b67bbf238b1e9c0afbc989d6e72c34d938b3b3f8953f60887c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:22.017092Z","signature_b64":"TM7rD7ePmzGDU0DX0h1D0M84A8867IEiU0mNTBYlLoBrgvI8kQwEj0ge+xcl9ZQhr958tim5JTMG3IGgAGiCBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb0d5613c3ccc7bed7aac307ba2798217047d867328e469dd23ff43e3baac972","last_reissued_at":"2026-07-05T10:46:22.016598Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:22.016598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"David Rozenberszki, Luca Wiehe, Luis Wiedmann","submitted_at":"2024-12-14T21:26:44Z","abstract_excerpt":"Open-set 3D segmentation represents a major point of interest for multiple downstream robotics and augmented/virtual reality applications. We present a decoupled 3D segmentation pipeline to ensure modularity and adaptability to novel 3D representations as well as semantic segmentation foundation models. We first reconstruct a scene with 3D Gaussians and learn class-agnostic features through contrastive supervision from a 2D instance proposal network. These 3D features are then clustered to form coarse object- or part-level masks. Finally, we match each 3D cluster to class-aware masks predicted"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.10972","kind":"arxiv","version":2},"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/2412.10972/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":"2412.10972","created_at":"2026-07-05T10:46:22.016658+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.10972v2","created_at":"2026-07-05T10:46:22.016658+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.10972","created_at":"2026-07-05T10:46:22.016658+00:00"},{"alias_kind":"pith_short_12","alias_value":"5MGVME6DZTD3","created_at":"2026-07-05T10:46:22.016658+00:00"},{"alias_kind":"pith_short_16","alias_value":"5MGVME6DZTD35V5K","created_at":"2026-07-05T10:46:22.016658+00:00"},{"alias_kind":"pith_short_8","alias_value":"5MGVME6D","created_at":"2026-07-05T10:46:22.016658+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/5MGVME6DZTD35V5KYMD3UJ4YEF","json":"https://pith.science/pith/5MGVME6DZTD35V5KYMD3UJ4YEF.json","graph_json":"https://pith.science/api/pith-number/5MGVME6DZTD35V5KYMD3UJ4YEF/graph.json","events_json":"https://pith.science/api/pith-number/5MGVME6DZTD35V5KYMD3UJ4YEF/events.json","paper":"https://pith.science/paper/5MGVME6D"},"agent_actions":{"view_html":"https://pith.science/pith/5MGVME6DZTD35V5KYMD3UJ4YEF","download_json":"https://pith.science/pith/5MGVME6DZTD35V5KYMD3UJ4YEF.json","view_paper":"https://pith.science/paper/5MGVME6D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.10972&json=true","fetch_graph":"https://pith.science/api/pith-number/5MGVME6DZTD35V5KYMD3UJ4YEF/graph.json","fetch_events":"https://pith.science/api/pith-number/5MGVME6DZTD35V5KYMD3UJ4YEF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5MGVME6DZTD35V5KYMD3UJ4YEF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5MGVME6DZTD35V5KYMD3UJ4YEF/action/storage_attestation","attest_author":"https://pith.science/pith/5MGVME6DZTD35V5KYMD3UJ4YEF/action/author_attestation","sign_citation":"https://pith.science/pith/5MGVME6DZTD35V5KYMD3UJ4YEF/action/citation_signature","submit_replication":"https://pith.science/pith/5MGVME6DZTD35V5KYMD3UJ4YEF/action/replication_record"}},"created_at":"2026-07-05T10:46:22.016658+00:00","updated_at":"2026-07-05T10:46:22.016658+00:00"}