{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BNDL3D6THTPERNI2E5DBEUA3AK","short_pith_number":"pith:BNDL3D6T","schema_version":"1.0","canonical_sha256":"0b46bd8fd33cde48b51a274612501b028c118ccc52a8443b0605f2104de08d41","source":{"kind":"arxiv","id":"2401.05345","version":1},"attestation_state":"computed","paper":{"title":"DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.GR","cs.PF"],"primary_cat":"cs.CV","authors_text":"Adrian Zhao, Fan Chen, Nandita Vijaykumar, Pawan Kumar Sanjaya, Ruofan Liang, Sankeerth Durvasula","submitted_at":"2023-12-01T19:56:08Z","abstract_excerpt":"Differentiable rendering is a technique used in an important emerging class of visual computing applications that involves representing a 3D scene as a model that is trained from 2D images using gradient descent. Recent works (e.g. 3D Gaussian Splatting) use a rasterization pipeline to enable rendering high quality photo-realistic imagery at high speeds from these learned 3D models. These methods have been demonstrated to be very promising, providing state-of-art quality for many important tasks. However, training a model to represent a scene is still a time-consuming task even when using powe"},"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":"2401.05345","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-01T19:56:08Z","cross_cats_sorted":["cs.GR","cs.PF"],"title_canon_sha256":"4c87af9aded538745f157fa93f00f74f51354095b8aaab696277813b55e58dec","abstract_canon_sha256":"55ecf30087dee8344f008465e9229cedaca144f046ea02a66ac5a64c0ab591c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:32:28.220198Z","signature_b64":"Dy4g2xuhgswRYE+PBeAjnXQpLlvWC3WY3ivM4K8ZC4qhg43o+9D2wDYZKoDzW309rLlEN3/QpXnULKXCBq3TBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b46bd8fd33cde48b51a274612501b028c118ccc52a8443b0605f2104de08d41","last_reissued_at":"2026-07-05T07:32:28.219715Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:32:28.219715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.GR","cs.PF"],"primary_cat":"cs.CV","authors_text":"Adrian Zhao, Fan Chen, Nandita Vijaykumar, Pawan Kumar Sanjaya, Ruofan Liang, Sankeerth Durvasula","submitted_at":"2023-12-01T19:56:08Z","abstract_excerpt":"Differentiable rendering is a technique used in an important emerging class of visual computing applications that involves representing a 3D scene as a model that is trained from 2D images using gradient descent. Recent works (e.g. 3D Gaussian Splatting) use a rasterization pipeline to enable rendering high quality photo-realistic imagery at high speeds from these learned 3D models. These methods have been demonstrated to be very promising, providing state-of-art quality for many important tasks. However, training a model to represent a scene is still a time-consuming task even when using powe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.05345","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/2401.05345/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":"2401.05345","created_at":"2026-07-05T07:32:28.219774+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.05345v1","created_at":"2026-07-05T07:32:28.219774+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05345","created_at":"2026-07-05T07:32:28.219774+00:00"},{"alias_kind":"pith_short_12","alias_value":"BNDL3D6THTPE","created_at":"2026-07-05T07:32:28.219774+00:00"},{"alias_kind":"pith_short_16","alias_value":"BNDL3D6THTPERNI2","created_at":"2026-07-05T07:32:28.219774+00:00"},{"alias_kind":"pith_short_8","alias_value":"BNDL3D6T","created_at":"2026-07-05T07:32:28.219774+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24796","citing_title":"Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2606.18588","citing_title":"Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00352","citing_title":"HiGS: A Hierarchical Rendering Architecture for Real-Time 3D Gaussian Splatting","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2412.13547","citing_title":"Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BNDL3D6THTPERNI2E5DBEUA3AK","json":"https://pith.science/pith/BNDL3D6THTPERNI2E5DBEUA3AK.json","graph_json":"https://pith.science/api/pith-number/BNDL3D6THTPERNI2E5DBEUA3AK/graph.json","events_json":"https://pith.science/api/pith-number/BNDL3D6THTPERNI2E5DBEUA3AK/events.json","paper":"https://pith.science/paper/BNDL3D6T"},"agent_actions":{"view_html":"https://pith.science/pith/BNDL3D6THTPERNI2E5DBEUA3AK","download_json":"https://pith.science/pith/BNDL3D6THTPERNI2E5DBEUA3AK.json","view_paper":"https://pith.science/paper/BNDL3D6T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.05345&json=true","fetch_graph":"https://pith.science/api/pith-number/BNDL3D6THTPERNI2E5DBEUA3AK/graph.json","fetch_events":"https://pith.science/api/pith-number/BNDL3D6THTPERNI2E5DBEUA3AK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BNDL3D6THTPERNI2E5DBEUA3AK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BNDL3D6THTPERNI2E5DBEUA3AK/action/storage_attestation","attest_author":"https://pith.science/pith/BNDL3D6THTPERNI2E5DBEUA3AK/action/author_attestation","sign_citation":"https://pith.science/pith/BNDL3D6THTPERNI2E5DBEUA3AK/action/citation_signature","submit_replication":"https://pith.science/pith/BNDL3D6THTPERNI2E5DBEUA3AK/action/replication_record"}},"created_at":"2026-07-05T07:32:28.219774+00:00","updated_at":"2026-07-05T07:32:28.219774+00:00"}