{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:P26XYSKUF5XDDYEKRGUADEJEOI","short_pith_number":"pith:P26XYSKU","schema_version":"1.0","canonical_sha256":"7ebd7c49542f6e31e08a89a80191247218a3f8551b67421c1ee4c49d7dbfdf4c","source":{"kind":"arxiv","id":"2606.20189","version":1},"attestation_state":"computed","paper":{"title":"HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-trainin","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Hariprasath Govindarajan, Jesper Ericsson, Maciej Wozniak, Olov Andersson, Patric Jensfelt, Thomas Gustafsson, Truls Nyberg","submitted_at":"2026-06-18T13:01:40Z","abstract_excerpt":"Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD). However, current approaches typically treat VFMs as black-box teachers, relying exclusively on frame-wise feature similarity. Consequently, they do not fully exploit the teacher's layer-wise semantic structure and global context, as well as the rich spatiotemporal information inherent in LiDAR sequences. We propose HilDA, a self-supervised pretr"},"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":"2606.20189","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-18T13:01:40Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"6553d4f74397422579e37123b0aa75dc608b4b913dfd7f5ecc9bbc5a5865884f","abstract_canon_sha256":"cc6d078b774ef1c57597fff4686ab266d0fdf90b2ddc38fa613d58e8b17d2401"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:13:05.182328Z","signature_b64":"Unz1mOMKwKjv0xIBw9c9lYrygh7/tkaQLDlJ9NQpNdooBlUqKmOAcCGKlQL9QoZn/ClAgNy9MIlchNqgt/N6AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ebd7c49542f6e31e08a89a80191247218a3f8551b67421c1ee4c49d7dbfdf4c","last_reissued_at":"2026-06-19T16:13:05.181899Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:13:05.181899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-trainin","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Hariprasath Govindarajan, Jesper Ericsson, Maciej Wozniak, Olov Andersson, Patric Jensfelt, Thomas Gustafsson, Truls Nyberg","submitted_at":"2026-06-18T13:01:40Z","abstract_excerpt":"Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD). However, current approaches typically treat VFMs as black-box teachers, relying exclusively on frame-wise feature similarity. Consequently, they do not fully exploit the teacher's layer-wise semantic structure and global context, as well as the rich spatiotemporal information inherent in LiDAR sequences. We propose HilDA, a self-supervised pretr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.20189","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/2606.20189/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":"2606.20189","created_at":"2026-06-19T16:13:05.181958+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.20189v1","created_at":"2026-06-19T16:13:05.181958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.20189","created_at":"2026-06-19T16:13:05.181958+00:00"},{"alias_kind":"pith_short_12","alias_value":"P26XYSKUF5XD","created_at":"2026-06-19T16:13:05.181958+00:00"},{"alias_kind":"pith_short_16","alias_value":"P26XYSKUF5XDDYEK","created_at":"2026-06-19T16:13:05.181958+00:00"},{"alias_kind":"pith_short_8","alias_value":"P26XYSKU","created_at":"2026-06-19T16:13:05.181958+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/P26XYSKUF5XDDYEKRGUADEJEOI","json":"https://pith.science/pith/P26XYSKUF5XDDYEKRGUADEJEOI.json","graph_json":"https://pith.science/api/pith-number/P26XYSKUF5XDDYEKRGUADEJEOI/graph.json","events_json":"https://pith.science/api/pith-number/P26XYSKUF5XDDYEKRGUADEJEOI/events.json","paper":"https://pith.science/paper/P26XYSKU"},"agent_actions":{"view_html":"https://pith.science/pith/P26XYSKUF5XDDYEKRGUADEJEOI","download_json":"https://pith.science/pith/P26XYSKUF5XDDYEKRGUADEJEOI.json","view_paper":"https://pith.science/paper/P26XYSKU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.20189&json=true","fetch_graph":"https://pith.science/api/pith-number/P26XYSKUF5XDDYEKRGUADEJEOI/graph.json","fetch_events":"https://pith.science/api/pith-number/P26XYSKUF5XDDYEKRGUADEJEOI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P26XYSKUF5XDDYEKRGUADEJEOI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P26XYSKUF5XDDYEKRGUADEJEOI/action/storage_attestation","attest_author":"https://pith.science/pith/P26XYSKUF5XDDYEKRGUADEJEOI/action/author_attestation","sign_citation":"https://pith.science/pith/P26XYSKUF5XDDYEKRGUADEJEOI/action/citation_signature","submit_replication":"https://pith.science/pith/P26XYSKUF5XDDYEKRGUADEJEOI/action/replication_record"}},"created_at":"2026-06-19T16:13:05.181958+00:00","updated_at":"2026-06-19T16:13:05.181958+00:00"}