{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:BSSN5PT2QSYLHUBWW2K3VET7SL","short_pith_number":"pith:BSSN5PT2","canonical_record":{"source":{"id":"2410.06893","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-09T13:57:39Z","cross_cats_sorted":[],"title_canon_sha256":"0d48b12f07201bdb93b6d47369ad6870cd70ed851c0f72447024ae2bebf635c3","abstract_canon_sha256":"c3324f77d72f596e9b419e68c8cbda90069051b70f1a7932d65db94e6c240be4"},"schema_version":"1.0"},"canonical_sha256":"0ca4debe7a84b0b3d036b695ba927f92f225a8ed8c65d6250ffaba69c0326668","source":{"kind":"arxiv","id":"2410.06893","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.06893","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"arxiv_version","alias_value":"2410.06893v1","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.06893","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"pith_short_12","alias_value":"BSSN5PT2QSYL","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"pith_short_16","alias_value":"BSSN5PT2QSYLHUBW","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"pith_short_8","alias_value":"BSSN5PT2","created_at":"2026-07-05T09:18:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:BSSN5PT2QSYLHUBWW2K3VET7SL","target":"record","payload":{"canonical_record":{"source":{"id":"2410.06893","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-09T13:57:39Z","cross_cats_sorted":[],"title_canon_sha256":"0d48b12f07201bdb93b6d47369ad6870cd70ed851c0f72447024ae2bebf635c3","abstract_canon_sha256":"c3324f77d72f596e9b419e68c8cbda90069051b70f1a7932d65db94e6c240be4"},"schema_version":"1.0"},"canonical_sha256":"0ca4debe7a84b0b3d036b695ba927f92f225a8ed8c65d6250ffaba69c0326668","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:18:08.273012Z","signature_b64":"fLqs0AaXkr0u1ift6ERt1bfxIboJJMojd7ud3616pabFxducaTK0hmR8LqTUXDmSrDK9ilsHhRlDBN0K6Bb0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ca4debe7a84b0b3d036b695ba927f92f225a8ed8c65d6250ffaba69c0326668","last_reissued_at":"2026-07-05T09:18:08.272494Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:18:08.272494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.06893","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":"2026-07-05T09:18:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KYFBYwHj5lNKak61HVFIn9ckrmzqG7YPCUDpXOEDOeHvjZ3lajRikGCGiUj5zevl3CUpNy/tBfrHVBR06UJWAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:35:47.669640Z"},"content_sha256":"b8ec75618ce25fbb30be09a7dd78402ee513466031ea51aa27d7d2fcdf6c8c55","schema_version":"1.0","event_id":"sha256:b8ec75618ce25fbb30be09a7dd78402ee513466031ea51aa27d7d2fcdf6c8c55"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:BSSN5PT2QSYLHUBWW2K3VET7SL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hwijeong Lee, Hyunjung Shim, Seungho Lee","submitted_at":"2024-10-09T13:57:39Z","abstract_excerpt":"We address the challenges of the semi-supervised LiDAR segmentation (SSLS) problem, particularly in low-budget scenarios. The two main issues in low-budget SSLS are the poor-quality pseudo-labels for unlabeled data, and the performance drops due to the significant imbalance between ground-truth and pseudo-labels. This imbalance leads to a vicious training cycle. To overcome these challenges, we leverage the spatio-temporal prior by recognizing the substantial overlap between temporally adjacent LiDAR scans. We propose a proximity-based label estimation, which generates highly accurate pseudo-l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.06893","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/2410.06893/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":"2026-07-05T09:18:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EENoqggr0FkQRApTKE2j3LJV33/HLofa7Cv7s6ti516hkOdRINQMRBAfx+0jv1kgx05yVJf48+TcKxOq5pVfBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:35:47.670161Z"},"content_sha256":"4e6dd001e4d51035684e4536bfe53e1ea834cc37e49f6ca455b5edbb9877455f","schema_version":"1.0","event_id":"sha256:4e6dd001e4d51035684e4536bfe53e1ea834cc37e49f6ca455b5edbb9877455f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BSSN5PT2QSYLHUBWW2K3VET7SL/bundle.json","state_url":"https://pith.science/pith/BSSN5PT2QSYLHUBWW2K3VET7SL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BSSN5PT2QSYLHUBWW2K3VET7SL/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":"2026-08-04T07:35:47Z","links":{"resolver":"https://pith.science/pith/BSSN5PT2QSYLHUBWW2K3VET7SL","bundle":"https://pith.science/pith/BSSN5PT2QSYLHUBWW2K3VET7SL/bundle.json","state":"https://pith.science/pith/BSSN5PT2QSYLHUBWW2K3VET7SL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BSSN5PT2QSYLHUBWW2K3VET7SL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BSSN5PT2QSYLHUBWW2K3VET7SL","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":"c3324f77d72f596e9b419e68c8cbda90069051b70f1a7932d65db94e6c240be4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-09T13:57:39Z","title_canon_sha256":"0d48b12f07201bdb93b6d47369ad6870cd70ed851c0f72447024ae2bebf635c3"},"schema_version":"1.0","source":{"id":"2410.06893","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.06893","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"arxiv_version","alias_value":"2410.06893v1","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.06893","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"pith_short_12","alias_value":"BSSN5PT2QSYL","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"pith_short_16","alias_value":"BSSN5PT2QSYLHUBW","created_at":"2026-07-05T09:18:08Z"},{"alias_kind":"pith_short_8","alias_value":"BSSN5PT2","created_at":"2026-07-05T09:18:08Z"}],"graph_snapshots":[{"event_id":"sha256:4e6dd001e4d51035684e4536bfe53e1ea834cc37e49f6ca455b5edbb9877455f","target":"graph","created_at":"2026-07-05T09:18:08Z","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/2410.06893/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We address the challenges of the semi-supervised LiDAR segmentation (SSLS) problem, particularly in low-budget scenarios. The two main issues in low-budget SSLS are the poor-quality pseudo-labels for unlabeled data, and the performance drops due to the significant imbalance between ground-truth and pseudo-labels. This imbalance leads to a vicious training cycle. To overcome these challenges, we leverage the spatio-temporal prior by recognizing the substantial overlap between temporally adjacent LiDAR scans. We propose a proximity-based label estimation, which generates highly accurate pseudo-l","authors_text":"Hwijeong Lee, Hyunjung Shim, Seungho Lee","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-09T13:57:39Z","title":"Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.06893","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:b8ec75618ce25fbb30be09a7dd78402ee513466031ea51aa27d7d2fcdf6c8c55","target":"record","created_at":"2026-07-05T09:18:08Z","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":"c3324f77d72f596e9b419e68c8cbda90069051b70f1a7932d65db94e6c240be4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-09T13:57:39Z","title_canon_sha256":"0d48b12f07201bdb93b6d47369ad6870cd70ed851c0f72447024ae2bebf635c3"},"schema_version":"1.0","source":{"id":"2410.06893","kind":"arxiv","version":1}},"canonical_sha256":"0ca4debe7a84b0b3d036b695ba927f92f225a8ed8c65d6250ffaba69c0326668","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ca4debe7a84b0b3d036b695ba927f92f225a8ed8c65d6250ffaba69c0326668","first_computed_at":"2026-07-05T09:18:08.272494Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:18:08.272494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fLqs0AaXkr0u1ift6ERt1bfxIboJJMojd7ud3616pabFxducaTK0hmR8LqTUXDmSrDK9ilsHhRlDBN0K6Bb0Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:18:08.273012Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.06893","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b8ec75618ce25fbb30be09a7dd78402ee513466031ea51aa27d7d2fcdf6c8c55","sha256:4e6dd001e4d51035684e4536bfe53e1ea834cc37e49f6ca455b5edbb9877455f"],"state_sha256":"87d37dad005e2782449aecff52682891bd8aade09183fee741945dcc405a0437"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hLEPq04udGSBwwwmBk16oLiwczBoRwpFqjV00mrLwDYyTly3H3VEkNMO+6g2xuInI8TCvpvjafX9sA03bgCxDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T07:35:47.675514Z","bundle_sha256":"faf88d68f193a69dcda9f7f107db70396dad3309b2811635cd24ad38793776e1"}}