{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6DZUNFUONNNFIT46C7EHOAPS5N","short_pith_number":"pith:6DZUNFUO","schema_version":"1.0","canonical_sha256":"f0f346968e6b5a544f9e17c87701f2eb62c6d65c8f338b8cb14244517daca7b7","source":{"kind":"arxiv","id":"2312.02480","version":2},"attestation_state":"computed","paper":{"title":"Differentiable Point-based Inverse Rendering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek","submitted_at":"2023-12-05T04:13:31Z","abstract_excerpt":"We present differentiable point-based inverse rendering, DPIR, an analysis-by-synthesis method that processes images captured under diverse illuminations to estimate shape and spatially-varying BRDF. To this end, we adopt point-based rendering, eliminating the need for multiple samplings per ray, typical of volumetric rendering, thus significantly enhancing the speed of inverse rendering. To realize this idea, we devise a hybrid point-volumetric representation for geometry and a regularized basis-BRDF representation for reflectance. The hybrid geometric representation enables fast rendering th"},"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":"2312.02480","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-05T04:13:31Z","cross_cats_sorted":[],"title_canon_sha256":"f1234b7e47377dc74aa6492b0893f5539b613431f1ace40a49df410edeb8b0ee","abstract_canon_sha256":"ee6c9693bfcbf4911bf92436322d94acaec70232f540df4121fc4386dd1b2fbd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:59:58.151176Z","signature_b64":"it6YjSjycHUVVadf+bZ6sr9fQtoSUGMWQGtbRmqmhlRry2mjzsL3SpaXvn74bwfX5ZO9/9K++btI9T/ledOxCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0f346968e6b5a544f9e17c87701f2eb62c6d65c8f338b8cb14244517daca7b7","last_reissued_at":"2026-07-05T07:59:58.150604Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:59:58.150604Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differentiable Point-based Inverse Rendering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek","submitted_at":"2023-12-05T04:13:31Z","abstract_excerpt":"We present differentiable point-based inverse rendering, DPIR, an analysis-by-synthesis method that processes images captured under diverse illuminations to estimate shape and spatially-varying BRDF. To this end, we adopt point-based rendering, eliminating the need for multiple samplings per ray, typical of volumetric rendering, thus significantly enhancing the speed of inverse rendering. To realize this idea, we devise a hybrid point-volumetric representation for geometry and a regularized basis-BRDF representation for reflectance. The hybrid geometric representation enables fast rendering th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02480","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/2312.02480/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":"2312.02480","created_at":"2026-07-05T07:59:58.150663+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.02480v2","created_at":"2026-07-05T07:59:58.150663+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02480","created_at":"2026-07-05T07:59:58.150663+00:00"},{"alias_kind":"pith_short_12","alias_value":"6DZUNFUONNNF","created_at":"2026-07-05T07:59:58.150663+00:00"},{"alias_kind":"pith_short_16","alias_value":"6DZUNFUONNNFIT46","created_at":"2026-07-05T07:59:58.150663+00:00"},{"alias_kind":"pith_short_8","alias_value":"6DZUNFUO","created_at":"2026-07-05T07:59:58.150663+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":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6DZUNFUONNNFIT46C7EHOAPS5N","json":"https://pith.science/pith/6DZUNFUONNNFIT46C7EHOAPS5N.json","graph_json":"https://pith.science/api/pith-number/6DZUNFUONNNFIT46C7EHOAPS5N/graph.json","events_json":"https://pith.science/api/pith-number/6DZUNFUONNNFIT46C7EHOAPS5N/events.json","paper":"https://pith.science/paper/6DZUNFUO"},"agent_actions":{"view_html":"https://pith.science/pith/6DZUNFUONNNFIT46C7EHOAPS5N","download_json":"https://pith.science/pith/6DZUNFUONNNFIT46C7EHOAPS5N.json","view_paper":"https://pith.science/paper/6DZUNFUO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.02480&json=true","fetch_graph":"https://pith.science/api/pith-number/6DZUNFUONNNFIT46C7EHOAPS5N/graph.json","fetch_events":"https://pith.science/api/pith-number/6DZUNFUONNNFIT46C7EHOAPS5N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6DZUNFUONNNFIT46C7EHOAPS5N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6DZUNFUONNNFIT46C7EHOAPS5N/action/storage_attestation","attest_author":"https://pith.science/pith/6DZUNFUONNNFIT46C7EHOAPS5N/action/author_attestation","sign_citation":"https://pith.science/pith/6DZUNFUONNNFIT46C7EHOAPS5N/action/citation_signature","submit_replication":"https://pith.science/pith/6DZUNFUONNNFIT46C7EHOAPS5N/action/replication_record"}},"created_at":"2026-07-05T07:59:58.150663+00:00","updated_at":"2026-07-05T07:59:58.150663+00:00"}