{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:BMHBBGPTH5UH34VMY3WO24DS3K","short_pith_number":"pith:BMHBBGPT","schema_version":"1.0","canonical_sha256":"0b0e1099f33f687df2acc6eced7072da83e6c7a91f6396846dc8c81dd81b9ce6","source":{"kind":"arxiv","id":"2012.01714","version":2},"attestation_state":"computed","paper":{"title":"AutoInt: Automatic Integration for Fast Neural Volume Rendering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"David B. Lindell, Gordon Wetzstein, Julien N. P. Martel","submitted_at":"2020-12-03T05:46:10Z","abstract_excerpt":"Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed t"},"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":"2012.01714","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-03T05:46:10Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"37430cbd588c7d4556dd8284bacbc1928ff32834141368353e9fa82862d3e8cd","abstract_canon_sha256":"0baac66fe0d134d919f59e992297c6527fec195d9d8a9137679e2af3eea68746"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:42:28.166460Z","signature_b64":"ZxT4Zbp6OQl1v0IbXrh23sZqDUEvFuD7JRPh6IJWdv1SMYn+WGY98MQIWFxMMJsWa4p3lgghydor5+3yfEG7Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b0e1099f33f687df2acc6eced7072da83e6c7a91f6396846dc8c81dd81b9ce6","last_reissued_at":"2026-07-05T02:42:28.165992Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:42:28.165992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AutoInt: Automatic Integration for Fast Neural Volume Rendering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"David B. Lindell, Gordon Wetzstein, Julien N. P. Martel","submitted_at":"2020-12-03T05:46:10Z","abstract_excerpt":"Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality. However, a fundamental obstacle to making these methods practical is the extreme computational and memory requirements caused by the required volume integrations along the rendered rays during training and inference. Millions of rays, each requiring hundreds of forward passes through a neural network are needed t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.01714","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/2012.01714/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":"2012.01714","created_at":"2026-07-05T02:42:28.166051+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.01714v2","created_at":"2026-07-05T02:42:28.166051+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.01714","created_at":"2026-07-05T02:42:28.166051+00:00"},{"alias_kind":"pith_short_12","alias_value":"BMHBBGPTH5UH","created_at":"2026-07-05T02:42:28.166051+00:00"},{"alias_kind":"pith_short_16","alias_value":"BMHBBGPTH5UH34VM","created_at":"2026-07-05T02:42:28.166051+00:00"},{"alias_kind":"pith_short_8","alias_value":"BMHBBGPT","created_at":"2026-07-05T02:42:28.166051+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/BMHBBGPTH5UH34VMY3WO24DS3K","json":"https://pith.science/pith/BMHBBGPTH5UH34VMY3WO24DS3K.json","graph_json":"https://pith.science/api/pith-number/BMHBBGPTH5UH34VMY3WO24DS3K/graph.json","events_json":"https://pith.science/api/pith-number/BMHBBGPTH5UH34VMY3WO24DS3K/events.json","paper":"https://pith.science/paper/BMHBBGPT"},"agent_actions":{"view_html":"https://pith.science/pith/BMHBBGPTH5UH34VMY3WO24DS3K","download_json":"https://pith.science/pith/BMHBBGPTH5UH34VMY3WO24DS3K.json","view_paper":"https://pith.science/paper/BMHBBGPT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.01714&json=true","fetch_graph":"https://pith.science/api/pith-number/BMHBBGPTH5UH34VMY3WO24DS3K/graph.json","fetch_events":"https://pith.science/api/pith-number/BMHBBGPTH5UH34VMY3WO24DS3K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BMHBBGPTH5UH34VMY3WO24DS3K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BMHBBGPTH5UH34VMY3WO24DS3K/action/storage_attestation","attest_author":"https://pith.science/pith/BMHBBGPTH5UH34VMY3WO24DS3K/action/author_attestation","sign_citation":"https://pith.science/pith/BMHBBGPTH5UH34VMY3WO24DS3K/action/citation_signature","submit_replication":"https://pith.science/pith/BMHBBGPTH5UH34VMY3WO24DS3K/action/replication_record"}},"created_at":"2026-07-05T02:42:28.166051+00:00","updated_at":"2026-07-05T02:42:28.166051+00:00"}