{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3NIQWIAGE5QMC53UOPSERWSQJY","short_pith_number":"pith:3NIQWIAG","canonical_record":{"source":{"id":"2312.15271","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T14:43:52Z","cross_cats_sorted":[],"title_canon_sha256":"034c7e80b7e4baea60b85ca9d58e1d745f41dbd6c83d09c2c68cd32cd5d7daa1","abstract_canon_sha256":"50128b18e13f0b9e192c162add42d1ef81efff2c5d2115b4ab0a195048d8b0e8"},"schema_version":"1.0"},"canonical_sha256":"db510b20062760c1777473e448da504e1884861653792266cde3ea4cdc40fe7d","source":{"kind":"arxiv","id":"2312.15271","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.15271","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"arxiv_version","alias_value":"2312.15271v2","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15271","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"pith_short_12","alias_value":"3NIQWIAGE5QM","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"3NIQWIAGE5QMC53U","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"3NIQWIAG","created_at":"2026-07-05T08:27:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3NIQWIAGE5QMC53UOPSERWSQJY","target":"record","payload":{"canonical_record":{"source":{"id":"2312.15271","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T14:43:52Z","cross_cats_sorted":[],"title_canon_sha256":"034c7e80b7e4baea60b85ca9d58e1d745f41dbd6c83d09c2c68cd32cd5d7daa1","abstract_canon_sha256":"50128b18e13f0b9e192c162add42d1ef81efff2c5d2115b4ab0a195048d8b0e8"},"schema_version":"1.0"},"canonical_sha256":"db510b20062760c1777473e448da504e1884861653792266cde3ea4cdc40fe7d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:01.834428Z","signature_b64":"0BQIbwb1Wa50JFuciv2h+CPfZvnaNECwToh4zbv0BmU6e6ETM93zuS2us9qygh6iQ1ljCHjWiUc/N/r2LlAHCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db510b20062760c1777473e448da504e1884861653792266cde3ea4cdc40fe7d","last_reissued_at":"2026-07-05T08:27:01.833852Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:01.833852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.15271","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:27:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lbs85SMjN4WID1Aasetg9pLVnne6Bgez4lWjO6UD9RVaqdUfZgIINH+nLAC4j0vZS26jKC6uexmTpL2gpix/Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T14:46:24.862489Z"},"content_sha256":"1df119e966e7c6fa11a44de3831b31391ad92b98625415bfa188e12b8a9476f4","schema_version":"1.0","event_id":"sha256:1df119e966e7c6fa11a44de3831b31391ad92b98625415bfa188e12b8a9476f4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3NIQWIAGE5QMC53UOPSERWSQJY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SSFlowNet: Semi-supervised Scene Flow Estimation On Point Clouds With Pseudo Label","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingze Chen, Junfeng Yao, Lei Li, Qiqin Lin, Rongzhou Zhou","submitted_at":"2023-12-23T14:43:52Z","abstract_excerpt":"In the domain of supervised scene flow estimation, the process of manual labeling is both time-intensive and financially demanding. This paper introduces SSFlowNet, a semi-supervised approach for scene flow estimation, that utilizes a blend of labeled and unlabeled data, optimizing the balance between the cost of labeling and the precision of model training. SSFlowNet stands out through its innovative use of pseudo-labels, mainly reducing the dependency on extensively labeled datasets while maintaining high model accuracy. The core of our model is its emphasis on the intricate geometric struct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15271","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/2312.15271/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":"2026-07-05T08:27:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q+jDW+d1m9xnUnwOumq/N7LRIIiOsnNHpwJOt8YA8XSNYM3jUmeL5LOx1cL6yZUVOqYKN92gj6X8VndxkwdsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T14:46:24.862859Z"},"content_sha256":"82548414ab59ff89e749afc8c929fc5e81351a28311d9cc185c93ce2bba85c7a","schema_version":"1.0","event_id":"sha256:82548414ab59ff89e749afc8c929fc5e81351a28311d9cc185c93ce2bba85c7a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3NIQWIAGE5QMC53UOPSERWSQJY/bundle.json","state_url":"https://pith.science/pith/3NIQWIAGE5QMC53UOPSERWSQJY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3NIQWIAGE5QMC53UOPSERWSQJY/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":"2026-07-17T14:46:24Z","links":{"resolver":"https://pith.science/pith/3NIQWIAGE5QMC53UOPSERWSQJY","bundle":"https://pith.science/pith/3NIQWIAGE5QMC53UOPSERWSQJY/bundle.json","state":"https://pith.science/pith/3NIQWIAGE5QMC53UOPSERWSQJY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3NIQWIAGE5QMC53UOPSERWSQJY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3NIQWIAGE5QMC53UOPSERWSQJY","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":"50128b18e13f0b9e192c162add42d1ef81efff2c5d2115b4ab0a195048d8b0e8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T14:43:52Z","title_canon_sha256":"034c7e80b7e4baea60b85ca9d58e1d745f41dbd6c83d09c2c68cd32cd5d7daa1"},"schema_version":"1.0","source":{"id":"2312.15271","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.15271","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"arxiv_version","alias_value":"2312.15271v2","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15271","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"pith_short_12","alias_value":"3NIQWIAGE5QM","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"3NIQWIAGE5QMC53U","created_at":"2026-07-05T08:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"3NIQWIAG","created_at":"2026-07-05T08:27:01Z"}],"graph_snapshots":[{"event_id":"sha256:82548414ab59ff89e749afc8c929fc5e81351a28311d9cc185c93ce2bba85c7a","target":"graph","created_at":"2026-07-05T08:27:01Z","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/2312.15271/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the domain of supervised scene flow estimation, the process of manual labeling is both time-intensive and financially demanding. This paper introduces SSFlowNet, a semi-supervised approach for scene flow estimation, that utilizes a blend of labeled and unlabeled data, optimizing the balance between the cost of labeling and the precision of model training. SSFlowNet stands out through its innovative use of pseudo-labels, mainly reducing the dependency on extensively labeled datasets while maintaining high model accuracy. The core of our model is its emphasis on the intricate geometric struct","authors_text":"Jingze Chen, Junfeng Yao, Lei Li, Qiqin Lin, Rongzhou Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T14:43:52Z","title":"SSFlowNet: Semi-supervised Scene Flow Estimation On Point Clouds With Pseudo Label"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15271","kind":"arxiv","version":2},"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":"sha256:1df119e966e7c6fa11a44de3831b31391ad92b98625415bfa188e12b8a9476f4","target":"record","created_at":"2026-07-05T08:27:01Z","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":"50128b18e13f0b9e192c162add42d1ef81efff2c5d2115b4ab0a195048d8b0e8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-23T14:43:52Z","title_canon_sha256":"034c7e80b7e4baea60b85ca9d58e1d745f41dbd6c83d09c2c68cd32cd5d7daa1"},"schema_version":"1.0","source":{"id":"2312.15271","kind":"arxiv","version":2}},"canonical_sha256":"db510b20062760c1777473e448da504e1884861653792266cde3ea4cdc40fe7d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db510b20062760c1777473e448da504e1884861653792266cde3ea4cdc40fe7d","first_computed_at":"2026-07-05T08:27:01.833852Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:27:01.833852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0BQIbwb1Wa50JFuciv2h+CPfZvnaNECwToh4zbv0BmU6e6ETM93zuS2us9qygh6iQ1ljCHjWiUc/N/r2LlAHCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:27:01.834428Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.15271","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1df119e966e7c6fa11a44de3831b31391ad92b98625415bfa188e12b8a9476f4","sha256:82548414ab59ff89e749afc8c929fc5e81351a28311d9cc185c93ce2bba85c7a"],"state_sha256":"59fcf898ddf0babf5c07adb2ac69cd49b78ae39ff4f39a573497a4b09b75167e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MIpVaMHzDlpeXtIbsXvp1Z2cfNyVPcHGs3c9gUELyxiF1fy4oMA155ASnEHzIJzGvR2Wpc6q8EP3+wQxriE6Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-17T14:46:24.865501Z","bundle_sha256":"0ef3a1adb786cd9cb0db884b46dbf44ba11c0a27d4566705a558216a4dbf7080"}}