{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OH6HU6IY6PFRLQLDZAYPKIFNNT","short_pith_number":"pith:OH6HU6IY","schema_version":"1.0","canonical_sha256":"71fc7a7918f3cb15c163c830f520ad6cd69193565b254dd58ec535b1465fdf25","source":{"kind":"arxiv","id":"1904.01786","version":1},"attestation_state":"computed","paper":{"title":"Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Li, Shichen Liu, Tianye Li, Weikai Chen","submitted_at":"2019-04-03T06:06:43Z","abstract_excerpt":"Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental discretization step called rasterization, which prevents the rendering process to be differentiable, hence able to be learned. Unlike the state-of-the-art differentiable renderers, which only approximate the rendering gradient in the back propagation, we propose a truly differentiable rendering framework that is able to ("},"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":"1904.01786","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-03T06:06:43Z","cross_cats_sorted":[],"title_canon_sha256":"aa44dd9495919db0f4c76f74de266e9d0ee5cd8468a2683a2d1365a2265056a8","abstract_canon_sha256":"20d40a739f2b73dd5faf1bb768f7cea43451626075ff1ddbc92bbe356e48b410"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:29.673465Z","signature_b64":"AhGXnAqFMvUlD41oRAvnVhy49o/zJ7Dr/oQxtQ2SC1WfrAA5zOT5U4veR+gG/Bo2QPC7dI2wEaaKo2aXRsBaDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71fc7a7918f3cb15c163c830f520ad6cd69193565b254dd58ec535b1465fdf25","last_reissued_at":"2026-05-17T23:49:29.672801Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:29.672801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Li, Shichen Liu, Tianye Li, Weikai Chen","submitted_at":"2019-04-03T06:06:43Z","abstract_excerpt":"Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental discretization step called rasterization, which prevents the rendering process to be differentiable, hence able to be learned. Unlike the state-of-the-art differentiable renderers, which only approximate the rendering gradient in the back propagation, we propose a truly differentiable rendering framework that is able to ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.01786","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":""},"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":"1904.01786","created_at":"2026-05-17T23:49:29.672900+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.01786v1","created_at":"2026-05-17T23:49:29.672900+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.01786","created_at":"2026-05-17T23:49:29.672900+00:00"},{"alias_kind":"pith_short_12","alias_value":"OH6HU6IY6PFR","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"OH6HU6IY6PFRLQLD","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"OH6HU6IY","created_at":"2026-05-18T12:33:24.271573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OH6HU6IY6PFRLQLDZAYPKIFNNT","json":"https://pith.science/pith/OH6HU6IY6PFRLQLDZAYPKIFNNT.json","graph_json":"https://pith.science/api/pith-number/OH6HU6IY6PFRLQLDZAYPKIFNNT/graph.json","events_json":"https://pith.science/api/pith-number/OH6HU6IY6PFRLQLDZAYPKIFNNT/events.json","paper":"https://pith.science/paper/OH6HU6IY"},"agent_actions":{"view_html":"https://pith.science/pith/OH6HU6IY6PFRLQLDZAYPKIFNNT","download_json":"https://pith.science/pith/OH6HU6IY6PFRLQLDZAYPKIFNNT.json","view_paper":"https://pith.science/paper/OH6HU6IY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.01786&json=true","fetch_graph":"https://pith.science/api/pith-number/OH6HU6IY6PFRLQLDZAYPKIFNNT/graph.json","fetch_events":"https://pith.science/api/pith-number/OH6HU6IY6PFRLQLDZAYPKIFNNT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OH6HU6IY6PFRLQLDZAYPKIFNNT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OH6HU6IY6PFRLQLDZAYPKIFNNT/action/storage_attestation","attest_author":"https://pith.science/pith/OH6HU6IY6PFRLQLDZAYPKIFNNT/action/author_attestation","sign_citation":"https://pith.science/pith/OH6HU6IY6PFRLQLDZAYPKIFNNT/action/citation_signature","submit_replication":"https://pith.science/pith/OH6HU6IY6PFRLQLDZAYPKIFNNT/action/replication_record"}},"created_at":"2026-05-17T23:49:29.672900+00:00","updated_at":"2026-05-17T23:49:29.672900+00:00"}