{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GYYM4IEJNVBVLEAI3ZJL5IY3NY","short_pith_number":"pith:GYYM4IEJ","schema_version":"1.0","canonical_sha256":"3630ce20896d43559008de52bea31b6e21e4155640a22978e10efed97df59355","source":{"kind":"arxiv","id":"2505.11521","version":1},"attestation_state":"computed","paper":{"title":"Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.CV","authors_text":"Lucas Mitchell, Mitra Hassani, Olivia Bennett, Omid Javadi, Shirin Rahimi, Sophie Carter, Wang Fang, Xu Lan","submitted_at":"2025-05-09T04:12:26Z","abstract_excerpt":"Point-cloud semantic segmentation underpins a wide range of critical applications. Although recent deep architectures and large-scale datasets have driven impressive closed-set performance, these models struggle to recognize or properly segment objects outside their training classes. This gap has sparked interest in Open-Set Semantic Segmentation (O3S), where models must both correctly label known categories and detect novel, unseen classes. In this paper, we propose a plug and play framework for O3S. By modeling the segmentation pipeline as a conditional Markov chain, we derive a novel regula"},"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":"2505.11521","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-09T04:12:26Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"33d5107218112ad9aa1abfcf9ef0680b86d6ccccc8d522430076441252b17b22","abstract_canon_sha256":"ba735166efdefe7a19e5e8efe479572368078a3b0f8890f46e33ee5f95c4e2ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:23.067392Z","signature_b64":"SbvzdHgI3kf8G7bs5N4eK21jebUQuLAP+O4owfHxZshERhegkVCOh99ofCaG6t+eBKP7RzsoahNGmLZRM8REDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3630ce20896d43559008de52bea31b6e21e4155640a22978e10efed97df59355","last_reissued_at":"2026-07-05T11:04:23.066907Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:23.066907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.CV","authors_text":"Lucas Mitchell, Mitra Hassani, Olivia Bennett, Omid Javadi, Shirin Rahimi, Sophie Carter, Wang Fang, Xu Lan","submitted_at":"2025-05-09T04:12:26Z","abstract_excerpt":"Point-cloud semantic segmentation underpins a wide range of critical applications. Although recent deep architectures and large-scale datasets have driven impressive closed-set performance, these models struggle to recognize or properly segment objects outside their training classes. This gap has sparked interest in Open-Set Semantic Segmentation (O3S), where models must both correctly label known categories and detect novel, unseen classes. In this paper, we propose a plug and play framework for O3S. By modeling the segmentation pipeline as a conditional Markov chain, we derive a novel regula"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11521","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/2505.11521/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":"2505.11521","created_at":"2026-07-05T11:04:23.066972+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.11521v1","created_at":"2026-07-05T11:04:23.066972+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11521","created_at":"2026-07-05T11:04:23.066972+00:00"},{"alias_kind":"pith_short_12","alias_value":"GYYM4IEJNVBV","created_at":"2026-07-05T11:04:23.066972+00:00"},{"alias_kind":"pith_short_16","alias_value":"GYYM4IEJNVBVLEAI","created_at":"2026-07-05T11:04:23.066972+00:00"},{"alias_kind":"pith_short_8","alias_value":"GYYM4IEJ","created_at":"2026-07-05T11:04:23.066972+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/GYYM4IEJNVBVLEAI3ZJL5IY3NY","json":"https://pith.science/pith/GYYM4IEJNVBVLEAI3ZJL5IY3NY.json","graph_json":"https://pith.science/api/pith-number/GYYM4IEJNVBVLEAI3ZJL5IY3NY/graph.json","events_json":"https://pith.science/api/pith-number/GYYM4IEJNVBVLEAI3ZJL5IY3NY/events.json","paper":"https://pith.science/paper/GYYM4IEJ"},"agent_actions":{"view_html":"https://pith.science/pith/GYYM4IEJNVBVLEAI3ZJL5IY3NY","download_json":"https://pith.science/pith/GYYM4IEJNVBVLEAI3ZJL5IY3NY.json","view_paper":"https://pith.science/paper/GYYM4IEJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.11521&json=true","fetch_graph":"https://pith.science/api/pith-number/GYYM4IEJNVBVLEAI3ZJL5IY3NY/graph.json","fetch_events":"https://pith.science/api/pith-number/GYYM4IEJNVBVLEAI3ZJL5IY3NY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GYYM4IEJNVBVLEAI3ZJL5IY3NY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GYYM4IEJNVBVLEAI3ZJL5IY3NY/action/storage_attestation","attest_author":"https://pith.science/pith/GYYM4IEJNVBVLEAI3ZJL5IY3NY/action/author_attestation","sign_citation":"https://pith.science/pith/GYYM4IEJNVBVLEAI3ZJL5IY3NY/action/citation_signature","submit_replication":"https://pith.science/pith/GYYM4IEJNVBVLEAI3ZJL5IY3NY/action/replication_record"}},"created_at":"2026-07-05T11:04:23.066972+00:00","updated_at":"2026-07-05T11:04:23.066972+00:00"}