{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VOGVOCT572FVOPCIA34FZEOPWO","short_pith_number":"pith:VOGVOCT5","schema_version":"1.0","canonical_sha256":"ab8d570a7dfe8b573c4806f85c91cfb3bfa66129edb241dd442a816a85362fa5","source":{"kind":"arxiv","id":"2411.17994","version":2},"attestation_state":"computed","paper":{"title":"Differentiable Inverse Rendering with Interpretable Basis BRDFs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek","submitted_at":"2024-11-27T02:22:14Z","abstract_excerpt":"Inverse rendering seeks to reconstruct both geometry and spatially varying BRDFs (SVBRDFs) from captured images. To address the inherent ill-posedness of inverse rendering, basis BRDF representations are commonly used, modeling SVBRDFs as spatially varying blends of a set of basis BRDFs. However, existing methods often yield basis BRDFs that lack intuitive separation and have limited scalability to scenes of varying complexity. In this paper, we introduce a differentiable inverse rendering method that produces interpretable basis BRDFs. Our approach models a scene using 2D Gaussians, where the"},"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":"2411.17994","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-27T02:22:14Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"421b68d7e9b2a7044ab9be929f0186f550c8d6091cbf73e61bc79bd554aae00a","abstract_canon_sha256":"b71620d4404850b466719684f84b5b330563171864fef97511ea7adc9792451c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:33.934951Z","signature_b64":"uJsGNtJ7ztBtsdMBVZ75zFK6P2lazJklfqABC/adOYAVDagP2T5Mx7jIocpL8eQ0V+GOizCl31zd4Z00gbSxDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab8d570a7dfe8b573c4806f85c91cfb3bfa66129edb241dd442a816a85362fa5","last_reissued_at":"2026-07-05T09:42:33.934365Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:33.934365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differentiable Inverse Rendering with Interpretable Basis BRDFs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek","submitted_at":"2024-11-27T02:22:14Z","abstract_excerpt":"Inverse rendering seeks to reconstruct both geometry and spatially varying BRDFs (SVBRDFs) from captured images. To address the inherent ill-posedness of inverse rendering, basis BRDF representations are commonly used, modeling SVBRDFs as spatially varying blends of a set of basis BRDFs. However, existing methods often yield basis BRDFs that lack intuitive separation and have limited scalability to scenes of varying complexity. In this paper, we introduce a differentiable inverse rendering method that produces interpretable basis BRDFs. Our approach models a scene using 2D Gaussians, where the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17994","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/2411.17994/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":"2411.17994","created_at":"2026-07-05T09:42:33.934479+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.17994v2","created_at":"2026-07-05T09:42:33.934479+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17994","created_at":"2026-07-05T09:42:33.934479+00:00"},{"alias_kind":"pith_short_12","alias_value":"VOGVOCT572FV","created_at":"2026-07-05T09:42:33.934479+00:00"},{"alias_kind":"pith_short_16","alias_value":"VOGVOCT572FVOPCI","created_at":"2026-07-05T09:42:33.934479+00:00"},{"alias_kind":"pith_short_8","alias_value":"VOGVOCT5","created_at":"2026-07-05T09:42:33.934479+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":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VOGVOCT572FVOPCIA34FZEOPWO","json":"https://pith.science/pith/VOGVOCT572FVOPCIA34FZEOPWO.json","graph_json":"https://pith.science/api/pith-number/VOGVOCT572FVOPCIA34FZEOPWO/graph.json","events_json":"https://pith.science/api/pith-number/VOGVOCT572FVOPCIA34FZEOPWO/events.json","paper":"https://pith.science/paper/VOGVOCT5"},"agent_actions":{"view_html":"https://pith.science/pith/VOGVOCT572FVOPCIA34FZEOPWO","download_json":"https://pith.science/pith/VOGVOCT572FVOPCIA34FZEOPWO.json","view_paper":"https://pith.science/paper/VOGVOCT5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.17994&json=true","fetch_graph":"https://pith.science/api/pith-number/VOGVOCT572FVOPCIA34FZEOPWO/graph.json","fetch_events":"https://pith.science/api/pith-number/VOGVOCT572FVOPCIA34FZEOPWO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VOGVOCT572FVOPCIA34FZEOPWO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VOGVOCT572FVOPCIA34FZEOPWO/action/storage_attestation","attest_author":"https://pith.science/pith/VOGVOCT572FVOPCIA34FZEOPWO/action/author_attestation","sign_citation":"https://pith.science/pith/VOGVOCT572FVOPCIA34FZEOPWO/action/citation_signature","submit_replication":"https://pith.science/pith/VOGVOCT572FVOPCIA34FZEOPWO/action/replication_record"}},"created_at":"2026-07-05T09:42:33.934479+00:00","updated_at":"2026-07-05T09:42:33.934479+00:00"}