{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2HYXDCYQVAUAQLFGH3KOQZZWCK","short_pith_number":"pith:2HYXDCYQ","schema_version":"1.0","canonical_sha256":"d1f1718b10a828082ca63ed4e867361283e6698a9b9cb02dd3b1d1f118bad637","source":{"kind":"arxiv","id":"2206.08077","version":1},"attestation_state":"computed","paper":{"title":"Neural Scene Representation for Locomotion on Structured Terrain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Animashree Anandkumar, Christopher Choy, David Hoeller, Marco Hutter, Nikita Rudin","submitted_at":"2022-06-16T10:45:17Z","abstract_excerpt":"We propose a learning-based method to reconstruct the local terrain for locomotion with a mobile robot traversing urban environments. Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the algorithm estimates the topography in the robot's vicinity. The raw measurements from these cameras are noisy and only provide partial and occluded observations that in many cases do not show the terrain the robot stands on. Therefore, we propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing"},"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":"2206.08077","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2022-06-16T10:45:17Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"7e153188038903a04913c849a61517037c87b656ac413aa6a957de3795fdd3d9","abstract_canon_sha256":"293ba4d93fe198cc360f30982e75201374070da1cc9897155927597c06f57523"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:32:21.925873Z","signature_b64":"0pCJ2gi9b2Nv7mOcAayHOOR/RQzgBGv9WoNeSD5qiMaZNOX/Ceh8M18ul1uP03ronkVlXkiyTwQCjURqOCoYDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1f1718b10a828082ca63ed4e867361283e6698a9b9cb02dd3b1d1f118bad637","last_reissued_at":"2026-07-05T04:32:21.925449Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:32:21.925449Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Scene Representation for Locomotion on Structured Terrain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Animashree Anandkumar, Christopher Choy, David Hoeller, Marco Hutter, Nikita Rudin","submitted_at":"2022-06-16T10:45:17Z","abstract_excerpt":"We propose a learning-based method to reconstruct the local terrain for locomotion with a mobile robot traversing urban environments. Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the algorithm estimates the topography in the robot's vicinity. The raw measurements from these cameras are noisy and only provide partial and occluded observations that in many cases do not show the terrain the robot stands on. Therefore, we propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.08077","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/2206.08077/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":"2206.08077","created_at":"2026-07-05T04:32:21.925509+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.08077v1","created_at":"2026-07-05T04:32:21.925509+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.08077","created_at":"2026-07-05T04:32:21.925509+00:00"},{"alias_kind":"pith_short_12","alias_value":"2HYXDCYQVAUA","created_at":"2026-07-05T04:32:21.925509+00:00"},{"alias_kind":"pith_short_16","alias_value":"2HYXDCYQVAUAQLFG","created_at":"2026-07-05T04:32:21.925509+00:00"},{"alias_kind":"pith_short_8","alias_value":"2HYXDCYQ","created_at":"2026-07-05T04:32:21.925509+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.03846","citing_title":"SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2HYXDCYQVAUAQLFGH3KOQZZWCK","json":"https://pith.science/pith/2HYXDCYQVAUAQLFGH3KOQZZWCK.json","graph_json":"https://pith.science/api/pith-number/2HYXDCYQVAUAQLFGH3KOQZZWCK/graph.json","events_json":"https://pith.science/api/pith-number/2HYXDCYQVAUAQLFGH3KOQZZWCK/events.json","paper":"https://pith.science/paper/2HYXDCYQ"},"agent_actions":{"view_html":"https://pith.science/pith/2HYXDCYQVAUAQLFGH3KOQZZWCK","download_json":"https://pith.science/pith/2HYXDCYQVAUAQLFGH3KOQZZWCK.json","view_paper":"https://pith.science/paper/2HYXDCYQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.08077&json=true","fetch_graph":"https://pith.science/api/pith-number/2HYXDCYQVAUAQLFGH3KOQZZWCK/graph.json","fetch_events":"https://pith.science/api/pith-number/2HYXDCYQVAUAQLFGH3KOQZZWCK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2HYXDCYQVAUAQLFGH3KOQZZWCK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2HYXDCYQVAUAQLFGH3KOQZZWCK/action/storage_attestation","attest_author":"https://pith.science/pith/2HYXDCYQVAUAQLFGH3KOQZZWCK/action/author_attestation","sign_citation":"https://pith.science/pith/2HYXDCYQVAUAQLFGH3KOQZZWCK/action/citation_signature","submit_replication":"https://pith.science/pith/2HYXDCYQVAUAQLFGH3KOQZZWCK/action/replication_record"}},"created_at":"2026-07-05T04:32:21.925509+00:00","updated_at":"2026-07-05T04:32:21.925509+00:00"}