{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NRGMU44KSPQU23QVDDASCRCLQN","short_pith_number":"pith:NRGMU44K","schema_version":"1.0","canonical_sha256":"6c4cca738a93e14d6e1518c121444b837c760aad121701d59fde3e6391524318","source":{"kind":"arxiv","id":"2504.12264","version":1},"attestation_state":"computed","paper":{"title":"Towards Learning to Complete Anything in Lidar","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aljo\\v{s}a O\\v{s}ep, Ayca Takmaz, Cristiano Saltori, Laura Leal-Taix\\'e, Neehar Peri, Riccardo de Lutio, Tim Meinhardt","submitted_at":"2025-04-16T17:21:55Z","abstract_excerpt":"We propose CAL (Complete Anything in Lidar) for Lidar-based shape-completion in-the-wild. This is closely related to Lidar-based semantic/panoptic scene completion. However, contemporary methods can only complete and recognize objects from a closed vocabulary labeled in existing Lidar datasets. Different to that, our zero-shot approach leverages the temporal context from multi-modal sensor sequences to mine object shapes and semantic features of observed objects. These are then distilled into a Lidar-only instance-level completion and recognition model. Although we only mine partial shape comp"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.12264","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-16T17:21:55Z","cross_cats_sorted":[],"title_canon_sha256":"e11b407c984a75bd287144daa7cb1799952a3aed9a2bba7dd087f34651139885","abstract_canon_sha256":"7688d8fb107cf5b8401696f435bc4d9577f5bb27125b9bd62de504245a302e70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:05.115531Z","signature_b64":"1WlA8BTavb5sUHyvtnnOTCgo7jiiMOAmvi0Cd3t1O0UIAS7EhKn5kBeqNhnIBl5UWMxsrU4TfpfL1UfErQTUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c4cca738a93e14d6e1518c121444b837c760aad121701d59fde3e6391524318","last_reissued_at":"2026-07-05T10:50:05.115051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:05.115051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Learning to Complete Anything in Lidar","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aljo\\v{s}a O\\v{s}ep, Ayca Takmaz, Cristiano Saltori, Laura Leal-Taix\\'e, Neehar Peri, Riccardo de Lutio, Tim Meinhardt","submitted_at":"2025-04-16T17:21:55Z","abstract_excerpt":"We propose CAL (Complete Anything in Lidar) for Lidar-based shape-completion in-the-wild. This is closely related to Lidar-based semantic/panoptic scene completion. However, contemporary methods can only complete and recognize objects from a closed vocabulary labeled in existing Lidar datasets. Different to that, our zero-shot approach leverages the temporal context from multi-modal sensor sequences to mine object shapes and semantic features of observed objects. These are then distilled into a Lidar-only instance-level completion and recognition model. Although we only mine partial shape comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12264","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/2504.12264/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.12264","created_at":"2026-07-05T10:50:05.115111+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.12264v1","created_at":"2026-07-05T10:50:05.115111+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12264","created_at":"2026-07-05T10:50:05.115111+00:00"},{"alias_kind":"pith_short_12","alias_value":"NRGMU44KSPQU","created_at":"2026-07-05T10:50:05.115111+00:00"},{"alias_kind":"pith_short_16","alias_value":"NRGMU44KSPQU23QV","created_at":"2026-07-05T10:50:05.115111+00:00"},{"alias_kind":"pith_short_8","alias_value":"NRGMU44K","created_at":"2026-07-05T10:50:05.115111+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NRGMU44KSPQU23QVDDASCRCLQN","json":"https://pith.science/pith/NRGMU44KSPQU23QVDDASCRCLQN.json","graph_json":"https://pith.science/api/pith-number/NRGMU44KSPQU23QVDDASCRCLQN/graph.json","events_json":"https://pith.science/api/pith-number/NRGMU44KSPQU23QVDDASCRCLQN/events.json","paper":"https://pith.science/paper/NRGMU44K"},"agent_actions":{"view_html":"https://pith.science/pith/NRGMU44KSPQU23QVDDASCRCLQN","download_json":"https://pith.science/pith/NRGMU44KSPQU23QVDDASCRCLQN.json","view_paper":"https://pith.science/paper/NRGMU44K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.12264&json=true","fetch_graph":"https://pith.science/api/pith-number/NRGMU44KSPQU23QVDDASCRCLQN/graph.json","fetch_events":"https://pith.science/api/pith-number/NRGMU44KSPQU23QVDDASCRCLQN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NRGMU44KSPQU23QVDDASCRCLQN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NRGMU44KSPQU23QVDDASCRCLQN/action/storage_attestation","attest_author":"https://pith.science/pith/NRGMU44KSPQU23QVDDASCRCLQN/action/author_attestation","sign_citation":"https://pith.science/pith/NRGMU44KSPQU23QVDDASCRCLQN/action/citation_signature","submit_replication":"https://pith.science/pith/NRGMU44KSPQU23QVDDASCRCLQN/action/replication_record"}},"created_at":"2026-07-05T10:50:05.115111+00:00","updated_at":"2026-07-05T10:50:05.115111+00:00"}