{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:4VDM6NAE5H2FHLAFJJ7E6N2I4S","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":"edc631b8d245ba956416541a23ade09fc4cc83ed98f9bfeaa92fda1dcfbdfa9b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-28T05:35:16Z","title_canon_sha256":"68f454c32f78936691ea63efc89eace347585337b2b8e59412a5ae6fbe218ab7"},"schema_version":"1.0","source":{"id":"2203.14508","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14508","created_at":"2026-07-05T04:09:05Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14508v1","created_at":"2026-07-05T04:09:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14508","created_at":"2026-07-05T04:09:05Z"},{"alias_kind":"pith_short_12","alias_value":"4VDM6NAE5H2F","created_at":"2026-07-05T04:09:05Z"},{"alias_kind":"pith_short_16","alias_value":"4VDM6NAE5H2FHLAF","created_at":"2026-07-05T04:09:05Z"},{"alias_kind":"pith_short_8","alias_value":"4VDM6NAE","created_at":"2026-07-05T04:09:05Z"}],"graph_snapshots":[{"event_id":"sha256:3e7436611d866dd95eb72686b49cb11d695b55e2cc1a20ce0f4f15aae9d4c8e2","target":"graph","created_at":"2026-07-05T04:09:05Z","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/2203.14508/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies. In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance. Specifically, we first put forward a novel key sampling strategy. For each query point, we sample nearby points densely and distant points sparsely as its keys in a stratified way, which enables the model to enlarge the effective receptive field and enjo","authors_text":"Hengshuang Zhao, Jianhui Liu, Jiaya Jia, Li Jiang, Liwei Wang, Shu Liu, Xiaojuan Qi, Xin Lai","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-28T05:35:16Z","title":"Stratified Transformer for 3D Point Cloud Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14508","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":"sha256:9a0294d34b2ffe6ff7e20bb0a0e0cfc48d508a9ea0c638ce40f42984bef861cc","target":"record","created_at":"2026-07-05T04:09:05Z","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":"edc631b8d245ba956416541a23ade09fc4cc83ed98f9bfeaa92fda1dcfbdfa9b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-28T05:35:16Z","title_canon_sha256":"68f454c32f78936691ea63efc89eace347585337b2b8e59412a5ae6fbe218ab7"},"schema_version":"1.0","source":{"id":"2203.14508","kind":"arxiv","version":1}},"canonical_sha256":"e546cf3404e9f453ac054a7e4f3748e4af596ba39b80d91e924b66946895e65a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e546cf3404e9f453ac054a7e4f3748e4af596ba39b80d91e924b66946895e65a","first_computed_at":"2026-07-05T04:09:05.860801Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:09:05.860801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1ypoXRBslW+Oi+tuj4PemUpK6rsw+zte4qRoBy/V2faqd+yHI2QGJX12NxG4tyhnd+XuHTVbbzlD4OUD0//PCw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:09:05.861260Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.14508","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a0294d34b2ffe6ff7e20bb0a0e0cfc48d508a9ea0c638ce40f42984bef861cc","sha256:3e7436611d866dd95eb72686b49cb11d695b55e2cc1a20ce0f4f15aae9d4c8e2"],"state_sha256":"3d13b839463facbab59858960c2a1f0422bfa7c44094e9b39940a71076d2f708"}