{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:WIEWV2JNXAAKHFD4UHRY4EFCQY","short_pith_number":"pith:WIEWV2JN","schema_version":"1.0","canonical_sha256":"b2096ae92db800a3947ca1e38e10a286342ed920067b836d1b9d34f8b33d0138","source":{"kind":"arxiv","id":"2211.12562","version":2},"attestation_state":"computed","paper":{"title":"PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces using Permutohedral Lattices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Radu Alexandru Rosu, Sven Behnke","submitted_at":"2022-11-22T20:27:44Z","abstract_excerpt":"Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent extension to hash-based positional encoding ensures fast training and inference with visually pleasing results. However, density-based methods struggle with recovering accurate surface geometry. Hybrid methods alleviate this issue by optimizing the density based on an underlying SDF. However, current SDF methods are overly smooth and miss fine geometric details. In this work, we combine the strengths of these two lines of work in a novel hash-based implicit surface represen"},"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":"2211.12562","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-22T20:27:44Z","cross_cats_sorted":[],"title_canon_sha256":"81ed104ae194e92540452d485c3803c7f9822aabcbec1bbacf5318d6c3f72b64","abstract_canon_sha256":"608a643db4d3e472787dac2b062195d7d2e3c0e1eb4dd959bcfd5c94ad17c442"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:55:14.876942Z","signature_b64":"l71MTPVLI8aSn6/cngB3iowE4zNKgZakKcCSl8+W+8MdJIgCY55boXy2UPsZX+Fp6czw56fsFO2caZCY4b1ZDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2096ae92db800a3947ca1e38e10a286342ed920067b836d1b9d34f8b33d0138","last_reissued_at":"2026-07-05T05:55:14.876488Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:55:14.876488Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces using Permutohedral Lattices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Radu Alexandru Rosu, Sven Behnke","submitted_at":"2022-11-22T20:27:44Z","abstract_excerpt":"Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent extension to hash-based positional encoding ensures fast training and inference with visually pleasing results. However, density-based methods struggle with recovering accurate surface geometry. Hybrid methods alleviate this issue by optimizing the density based on an underlying SDF. However, current SDF methods are overly smooth and miss fine geometric details. In this work, we combine the strengths of these two lines of work in a novel hash-based implicit surface represen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.12562","kind":"arxiv","version":2},"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/2211.12562/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":"2211.12562","created_at":"2026-07-05T05:55:14.876546+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.12562v2","created_at":"2026-07-05T05:55:14.876546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.12562","created_at":"2026-07-05T05:55:14.876546+00:00"},{"alias_kind":"pith_short_12","alias_value":"WIEWV2JNXAAK","created_at":"2026-07-05T05:55:14.876546+00:00"},{"alias_kind":"pith_short_16","alias_value":"WIEWV2JNXAAKHFD4","created_at":"2026-07-05T05:55:14.876546+00:00"},{"alias_kind":"pith_short_8","alias_value":"WIEWV2JN","created_at":"2026-07-05T05:55:14.876546+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.09604","citing_title":"A Hybrid Neural-Microfacet BRDF Model for Real-Time Rendering","ref_index":94,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WIEWV2JNXAAKHFD4UHRY4EFCQY","json":"https://pith.science/pith/WIEWV2JNXAAKHFD4UHRY4EFCQY.json","graph_json":"https://pith.science/api/pith-number/WIEWV2JNXAAKHFD4UHRY4EFCQY/graph.json","events_json":"https://pith.science/api/pith-number/WIEWV2JNXAAKHFD4UHRY4EFCQY/events.json","paper":"https://pith.science/paper/WIEWV2JN"},"agent_actions":{"view_html":"https://pith.science/pith/WIEWV2JNXAAKHFD4UHRY4EFCQY","download_json":"https://pith.science/pith/WIEWV2JNXAAKHFD4UHRY4EFCQY.json","view_paper":"https://pith.science/paper/WIEWV2JN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.12562&json=true","fetch_graph":"https://pith.science/api/pith-number/WIEWV2JNXAAKHFD4UHRY4EFCQY/graph.json","fetch_events":"https://pith.science/api/pith-number/WIEWV2JNXAAKHFD4UHRY4EFCQY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WIEWV2JNXAAKHFD4UHRY4EFCQY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WIEWV2JNXAAKHFD4UHRY4EFCQY/action/storage_attestation","attest_author":"https://pith.science/pith/WIEWV2JNXAAKHFD4UHRY4EFCQY/action/author_attestation","sign_citation":"https://pith.science/pith/WIEWV2JNXAAKHFD4UHRY4EFCQY/action/citation_signature","submit_replication":"https://pith.science/pith/WIEWV2JNXAAKHFD4UHRY4EFCQY/action/replication_record"}},"created_at":"2026-07-05T05:55:14.876546+00:00","updated_at":"2026-07-05T05:55:14.876546+00:00"}