{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:JO6Z3WUB7QOAAPQUXHD3AHFY77","short_pith_number":"pith:JO6Z3WUB","canonical_record":{"source":{"id":"2210.09948","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-18T15:57:20Z","cross_cats_sorted":[],"title_canon_sha256":"f1e8d7282643bdac93130d58bed37796177ab7fafd84989f891c0382a2b36c81","abstract_canon_sha256":"fb6d341f649baac18d89dcf629620b3fed404f2bd587696e5a86fa3ffdda755c"},"schema_version":"1.0"},"canonical_sha256":"4bbd9dda81fc1c003e14b9c7b01cb8ffcb802abc5cbf8efe3471ca683d0c2839","source":{"kind":"arxiv","id":"2210.09948","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.09948","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"arxiv_version","alias_value":"2210.09948v1","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.09948","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"pith_short_12","alias_value":"JO6Z3WUB7QOA","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"pith_short_16","alias_value":"JO6Z3WUB7QOAAPQU","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"pith_short_8","alias_value":"JO6Z3WUB","created_at":"2026-07-05T05:08:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:JO6Z3WUB7QOAAPQUXHD3AHFY77","target":"record","payload":{"canonical_record":{"source":{"id":"2210.09948","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-18T15:57:20Z","cross_cats_sorted":[],"title_canon_sha256":"f1e8d7282643bdac93130d58bed37796177ab7fafd84989f891c0382a2b36c81","abstract_canon_sha256":"fb6d341f649baac18d89dcf629620b3fed404f2bd587696e5a86fa3ffdda755c"},"schema_version":"1.0"},"canonical_sha256":"4bbd9dda81fc1c003e14b9c7b01cb8ffcb802abc5cbf8efe3471ca683d0c2839","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:08:01.194746Z","signature_b64":"pEeH6k2veUQmKb0sQzGS6OxIggXd5y4TDQbfsHbgYmcWV1ipc8OkCW3kPTaE3GE2YkdazZk3Ph4+t5On1WVbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4bbd9dda81fc1c003e14b9c7b01cb8ffcb802abc5cbf8efe3471ca683d0c2839","last_reissued_at":"2026-07-05T05:08:01.194354Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:08:01.194354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.09948","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:08:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Tqo2vBNsOmEQmhnzSyhFVm4C4He5CAsTcGTouenGF29WmPlKQAffOgVc376pSMEp6LYIbTEQQfB51jWlRthBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:56:46.071638Z"},"content_sha256":"70189cccbd428dbca34d10d4b36e1dc4be6301b47d82addd869d699579fe6360","schema_version":"1.0","event_id":"sha256:70189cccbd428dbca34d10d4b36e1dc4be6301b47d82addd869d699579fe6360"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:JO6Z3WUB7QOAAPQUXHD3AHFY77","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ce Zhang, Jun Wang, Siheng Chen, Xiaolong Li, Yanfeng Wang, Yangheng Zhao, Yue Hu","submitted_at":"2022-10-18T15:57:20Z","abstract_excerpt":"3D point cloud semantic segmentation is one of the fundamental tasks for 3D scene understanding and has been widely used in the metaverse applications. Many recent 3D semantic segmentation methods learn a single prototype (classifier weights) for each semantic class, and classify 3D points according to their nearest prototype. However, learning only one prototype for each class limits the model's ability to describe the high variance patterns within a class. Instead of learning a single prototype for each class, in this paper, we propose to use an adaptive number of prototypes to dynamically d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.09948","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/2210.09948/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:08:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zFNPB/jbfXpVGKVpX/zrlHR9/BlFxU3KBsLBzGWsLo/GdibwB89O+0tQQHaJ+nOsOgmlV0yBjyyV1NpwtzG1BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:56:46.072004Z"},"content_sha256":"126398b163a6669f397f3029061b1510a24401092d4c6c1a8140c9ffac63f784","schema_version":"1.0","event_id":"sha256:126398b163a6669f397f3029061b1510a24401092d4c6c1a8140c9ffac63f784"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JO6Z3WUB7QOAAPQUXHD3AHFY77/bundle.json","state_url":"https://pith.science/pith/JO6Z3WUB7QOAAPQUXHD3AHFY77/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JO6Z3WUB7QOAAPQUXHD3AHFY77/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":"2026-07-06T20:56:46Z","links":{"resolver":"https://pith.science/pith/JO6Z3WUB7QOAAPQUXHD3AHFY77","bundle":"https://pith.science/pith/JO6Z3WUB7QOAAPQUXHD3AHFY77/bundle.json","state":"https://pith.science/pith/JO6Z3WUB7QOAAPQUXHD3AHFY77/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JO6Z3WUB7QOAAPQUXHD3AHFY77/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:JO6Z3WUB7QOAAPQUXHD3AHFY77","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":"fb6d341f649baac18d89dcf629620b3fed404f2bd587696e5a86fa3ffdda755c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-18T15:57:20Z","title_canon_sha256":"f1e8d7282643bdac93130d58bed37796177ab7fafd84989f891c0382a2b36c81"},"schema_version":"1.0","source":{"id":"2210.09948","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.09948","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"arxiv_version","alias_value":"2210.09948v1","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.09948","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"pith_short_12","alias_value":"JO6Z3WUB7QOA","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"pith_short_16","alias_value":"JO6Z3WUB7QOAAPQU","created_at":"2026-07-05T05:08:01Z"},{"alias_kind":"pith_short_8","alias_value":"JO6Z3WUB","created_at":"2026-07-05T05:08:01Z"}],"graph_snapshots":[{"event_id":"sha256:126398b163a6669f397f3029061b1510a24401092d4c6c1a8140c9ffac63f784","target":"graph","created_at":"2026-07-05T05:08:01Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2210.09948/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"3D point cloud semantic segmentation is one of the fundamental tasks for 3D scene understanding and has been widely used in the metaverse applications. Many recent 3D semantic segmentation methods learn a single prototype (classifier weights) for each semantic class, and classify 3D points according to their nearest prototype. However, learning only one prototype for each class limits the model's ability to describe the high variance patterns within a class. Instead of learning a single prototype for each class, in this paper, we propose to use an adaptive number of prototypes to dynamically d","authors_text":"Ce Zhang, Jun Wang, Siheng Chen, Xiaolong Li, Yanfeng Wang, Yangheng Zhao, Yue Hu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-18T15:57:20Z","title":"Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.09948","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:70189cccbd428dbca34d10d4b36e1dc4be6301b47d82addd869d699579fe6360","target":"record","created_at":"2026-07-05T05:08:01Z","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":"fb6d341f649baac18d89dcf629620b3fed404f2bd587696e5a86fa3ffdda755c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-18T15:57:20Z","title_canon_sha256":"f1e8d7282643bdac93130d58bed37796177ab7fafd84989f891c0382a2b36c81"},"schema_version":"1.0","source":{"id":"2210.09948","kind":"arxiv","version":1}},"canonical_sha256":"4bbd9dda81fc1c003e14b9c7b01cb8ffcb802abc5cbf8efe3471ca683d0c2839","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4bbd9dda81fc1c003e14b9c7b01cb8ffcb802abc5cbf8efe3471ca683d0c2839","first_computed_at":"2026-07-05T05:08:01.194354Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:08:01.194354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pEeH6k2veUQmKb0sQzGS6OxIggXd5y4TDQbfsHbgYmcWV1ipc8OkCW3kPTaE3GE2YkdazZk3Ph4+t5On1WVbDw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:08:01.194746Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.09948","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70189cccbd428dbca34d10d4b36e1dc4be6301b47d82addd869d699579fe6360","sha256:126398b163a6669f397f3029061b1510a24401092d4c6c1a8140c9ffac63f784"],"state_sha256":"eefacd90b273b91f30a1e04b7303ed403c792d3c1e24566584c4c2ea929c5ddb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wNdH2DuC/RWt604hPrJRVWgG+Whxp9g5HT4vldMxZ8i2+wtScEKOT9Yx0laB+5mWFi/2T+87kIG84pE6g+p0CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T20:56:46.082361Z","bundle_sha256":"fb5f804b6340993075a3a3b24eb2282a347b7f750996faf2437e0c65766144fb"}}