{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:SEAXRYDKC7G3UVHLVURO4FVZSM","short_pith_number":"pith:SEAXRYDK","canonical_record":{"source":{"id":"2505.03300","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-06T08:31:32Z","cross_cats_sorted":[],"title_canon_sha256":"05011acdb1e12de58d5c081f8c02702b3f77f6780505d664635222a613f6a44e","abstract_canon_sha256":"7c4acbaa4a8dac1428b784493383dde7cb0bf7362181edbd9161d07547d54996"},"schema_version":"1.0"},"canonical_sha256":"910178e06a17cdba54ebad22ee16b9933ec803f8ca29eb8a460467d2cc6cd315","source":{"kind":"arxiv","id":"2505.03300","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.03300","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"arxiv_version","alias_value":"2505.03300v1","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.03300","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"pith_short_12","alias_value":"SEAXRYDKC7G3","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"pith_short_16","alias_value":"SEAXRYDKC7G3UVHL","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"pith_short_8","alias_value":"SEAXRYDK","created_at":"2026-07-05T10:59:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:SEAXRYDKC7G3UVHLVURO4FVZSM","target":"record","payload":{"canonical_record":{"source":{"id":"2505.03300","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-06T08:31:32Z","cross_cats_sorted":[],"title_canon_sha256":"05011acdb1e12de58d5c081f8c02702b3f77f6780505d664635222a613f6a44e","abstract_canon_sha256":"7c4acbaa4a8dac1428b784493383dde7cb0bf7362181edbd9161d07547d54996"},"schema_version":"1.0"},"canonical_sha256":"910178e06a17cdba54ebad22ee16b9933ec803f8ca29eb8a460467d2cc6cd315","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:13.581478Z","signature_b64":"cP9vyb5CUwOFcC9iK0zV0HcO40mKiNIMo5uwbGL2xwSbSsb6hORajBxs+jaD6OTvVkhcEIzCc0TZwqBK+5C0Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"910178e06a17cdba54ebad22ee16b9933ec803f8ca29eb8a460467d2cc6cd315","last_reissued_at":"2026-07-05T10:59:13.580958Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:13.580958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.03300","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":"2026-07-05T10:59:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4ln0XtPZrefKe1hi7wV49RV6kJUUvvcT9jKJW6qVb80AurGeQX4HyfxBGT3XotgC6oPhTJNyR/AL4lIudSDPCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T06:00:29.856873Z"},"content_sha256":"8eb6b0daca443b23acbfb4091ad3e96a1b9bd96208e44cc0ab002244ce3834f1","schema_version":"1.0","event_id":"sha256:8eb6b0daca443b23acbfb4091ad3e96a1b9bd96208e44cc0ab002244ce3834f1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:SEAXRYDKC7G3UVHLVURO4FVZSM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Caunes, Thierry Chateau, Vincent Fr\\'emont","submitted_at":"2025-05-06T08:31:32Z","abstract_excerpt":"Semantic segmentation of 3D LiDAR point clouds, essential for autonomous driving and infrastructure management, is best achieved by supervised learning, which demands extensive annotated datasets and faces the problem of domain shifts. We introduce a new 3D semantic segmentation pipeline that leverages aligned scenes and state-of-the-art 2D segmentation methods, avoiding the need for direct 3D annotation or reliance on additional modalities such as camera images at inference time. Our approach generates 2D views from LiDAR scans colored by sensor intensity and applies 2D semantic segmentation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.03300","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/2505.03300/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":"2026-07-05T10:59:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JooV1o4UOOe+NQQRrAg/lp1euozGP6aooQcrlQbzu7aJPFH5uwykLZ5UxQG0gkQTvy0HNk2wS6643yEJ2d3bCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T06:00:29.857559Z"},"content_sha256":"b279e2815a6dcb808feffb2eab71093ef08ddf043a1942d9f20efc0a8e725aa8","schema_version":"1.0","event_id":"sha256:b279e2815a6dcb808feffb2eab71093ef08ddf043a1942d9f20efc0a8e725aa8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SEAXRYDKC7G3UVHLVURO4FVZSM/bundle.json","state_url":"https://pith.science/pith/SEAXRYDKC7G3UVHLVURO4FVZSM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SEAXRYDKC7G3UVHLVURO4FVZSM/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":"2026-08-18T06:00:29Z","links":{"resolver":"https://pith.science/pith/SEAXRYDKC7G3UVHLVURO4FVZSM","bundle":"https://pith.science/pith/SEAXRYDKC7G3UVHLVURO4FVZSM/bundle.json","state":"https://pith.science/pith/SEAXRYDKC7G3UVHLVURO4FVZSM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SEAXRYDKC7G3UVHLVURO4FVZSM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SEAXRYDKC7G3UVHLVURO4FVZSM","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":"7c4acbaa4a8dac1428b784493383dde7cb0bf7362181edbd9161d07547d54996","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-06T08:31:32Z","title_canon_sha256":"05011acdb1e12de58d5c081f8c02702b3f77f6780505d664635222a613f6a44e"},"schema_version":"1.0","source":{"id":"2505.03300","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.03300","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"arxiv_version","alias_value":"2505.03300v1","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.03300","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"pith_short_12","alias_value":"SEAXRYDKC7G3","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"pith_short_16","alias_value":"SEAXRYDKC7G3UVHL","created_at":"2026-07-05T10:59:13Z"},{"alias_kind":"pith_short_8","alias_value":"SEAXRYDK","created_at":"2026-07-05T10:59:13Z"}],"graph_snapshots":[{"event_id":"sha256:b279e2815a6dcb808feffb2eab71093ef08ddf043a1942d9f20efc0a8e725aa8","target":"graph","created_at":"2026-07-05T10:59:13Z","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/2505.03300/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semantic segmentation of 3D LiDAR point clouds, essential for autonomous driving and infrastructure management, is best achieved by supervised learning, which demands extensive annotated datasets and faces the problem of domain shifts. We introduce a new 3D semantic segmentation pipeline that leverages aligned scenes and state-of-the-art 2D segmentation methods, avoiding the need for direct 3D annotation or reliance on additional modalities such as camera images at inference time. Our approach generates 2D views from LiDAR scans colored by sensor intensity and applies 2D semantic segmentation ","authors_text":"Andrew Caunes, Thierry Chateau, Vincent Fr\\'emont","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-06T08:31:32Z","title":"3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.03300","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":"sha256:8eb6b0daca443b23acbfb4091ad3e96a1b9bd96208e44cc0ab002244ce3834f1","target":"record","created_at":"2026-07-05T10:59:13Z","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":"7c4acbaa4a8dac1428b784493383dde7cb0bf7362181edbd9161d07547d54996","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-06T08:31:32Z","title_canon_sha256":"05011acdb1e12de58d5c081f8c02702b3f77f6780505d664635222a613f6a44e"},"schema_version":"1.0","source":{"id":"2505.03300","kind":"arxiv","version":1}},"canonical_sha256":"910178e06a17cdba54ebad22ee16b9933ec803f8ca29eb8a460467d2cc6cd315","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"910178e06a17cdba54ebad22ee16b9933ec803f8ca29eb8a460467d2cc6cd315","first_computed_at":"2026-07-05T10:59:13.580958Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:59:13.580958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cP9vyb5CUwOFcC9iK0zV0HcO40mKiNIMo5uwbGL2xwSbSsb6hORajBxs+jaD6OTvVkhcEIzCc0TZwqBK+5C0Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:59:13.581478Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.03300","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8eb6b0daca443b23acbfb4091ad3e96a1b9bd96208e44cc0ab002244ce3834f1","sha256:b279e2815a6dcb808feffb2eab71093ef08ddf043a1942d9f20efc0a8e725aa8"],"state_sha256":"0607224b24e31c9ddaa79b999f5b2ec050ba1ec303d4ac963d0e0cc723b4ae3e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WWOnM6nLzgHjsAIu+T6Yry3f6NSolE99lsU+19yGVXzLtRjWeowO1tSeWgiH6NEizAViCZPwuo2Jx+DlWn7lDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T06:00:29.865618Z","bundle_sha256":"271760d6c8af5d4a5039f12bb3cd65665843d7c3dfcbddc56858ccc95961f7a3"}}