{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4TUW4LIPOIAFTFFFH5OSZDKNJL","short_pith_number":"pith:4TUW4LIP","schema_version":"1.0","canonical_sha256":"e4e96e2d0f72005994a53f5d2c8d4d4afc2ad48b89c7ff3782fcac28c165b9b1","source":{"kind":"arxiv","id":"2208.00277","version":5},"attestation_state":"computed","paper":{"title":"MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Andrea Tagliasacchi, Peter Hedman, Thomas Funkhouser, Zhiqin Chen","submitted_at":"2022-07-30T17:14:14Z","abstract_excerpt":"Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray marching that are mismatched to the capabilities of widely deployed graphics hardware. This paper introduces a new NeRF representation based on textured polygons that can synthesize novel images efficiently with standard rendering pipelines. The NeRF is represented as a set of polygons with textures representing binary opacities and feature vectors. Traditional rendering of the polygons with a z-buf"},"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":"2208.00277","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-30T17:14:14Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"a990ba80fce8f34563e7756b822a7eef67328cd30f1ef6c36c87faef2fd85867","abstract_canon_sha256":"61226247e43c93fcd1500a8329b385eee1e06b3472b3169613588acb5c5f265b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:56.255864Z","signature_b64":"d7exRfuRgmgodKnIfRDaM+82TZTWmIG6kPfLOcjhCkcu4sNdB9e6q7afvvbLmHDkB31G959tQ3YOjGumesHNDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4e96e2d0f72005994a53f5d2c8d4d4afc2ad48b89c7ff3782fcac28c165b9b1","last_reissued_at":"2026-07-05T06:14:56.255383Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:56.255383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Andrea Tagliasacchi, Peter Hedman, Thomas Funkhouser, Zhiqin Chen","submitted_at":"2022-07-30T17:14:14Z","abstract_excerpt":"Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray marching that are mismatched to the capabilities of widely deployed graphics hardware. This paper introduces a new NeRF representation based on textured polygons that can synthesize novel images efficiently with standard rendering pipelines. The NeRF is represented as a set of polygons with textures representing binary opacities and feature vectors. Traditional rendering of the polygons with a z-buf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.00277","kind":"arxiv","version":5},"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/2208.00277/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":"2208.00277","created_at":"2026-07-05T06:14:56.255440+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.00277v5","created_at":"2026-07-05T06:14:56.255440+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.00277","created_at":"2026-07-05T06:14:56.255440+00:00"},{"alias_kind":"pith_short_12","alias_value":"4TUW4LIPOIAF","created_at":"2026-07-05T06:14:56.255440+00:00"},{"alias_kind":"pith_short_16","alias_value":"4TUW4LIPOIAFTFFF","created_at":"2026-07-05T06:14:56.255440+00:00"},{"alias_kind":"pith_short_8","alias_value":"4TUW4LIP","created_at":"2026-07-05T06:14:56.255440+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.18156","citing_title":"ReAge3D: Re-Aging 3D Faces with View Consistency","ref_index":187,"is_internal_anchor":false},{"citing_arxiv_id":"2205.13524","citing_title":"PREF: Phasorial Embedding Fields for Compact Neural Representations","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2309.16653","citing_title":"DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation","ref_index":91,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08824","citing_title":"HairGPT: Strand-as-Language Autoregressive Modeling for Realistic 3D Hairstyle Synthesis","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4TUW4LIPOIAFTFFFH5OSZDKNJL","json":"https://pith.science/pith/4TUW4LIPOIAFTFFFH5OSZDKNJL.json","graph_json":"https://pith.science/api/pith-number/4TUW4LIPOIAFTFFFH5OSZDKNJL/graph.json","events_json":"https://pith.science/api/pith-number/4TUW4LIPOIAFTFFFH5OSZDKNJL/events.json","paper":"https://pith.science/paper/4TUW4LIP"},"agent_actions":{"view_html":"https://pith.science/pith/4TUW4LIPOIAFTFFFH5OSZDKNJL","download_json":"https://pith.science/pith/4TUW4LIPOIAFTFFFH5OSZDKNJL.json","view_paper":"https://pith.science/paper/4TUW4LIP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.00277&json=true","fetch_graph":"https://pith.science/api/pith-number/4TUW4LIPOIAFTFFFH5OSZDKNJL/graph.json","fetch_events":"https://pith.science/api/pith-number/4TUW4LIPOIAFTFFFH5OSZDKNJL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4TUW4LIPOIAFTFFFH5OSZDKNJL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4TUW4LIPOIAFTFFFH5OSZDKNJL/action/storage_attestation","attest_author":"https://pith.science/pith/4TUW4LIPOIAFTFFFH5OSZDKNJL/action/author_attestation","sign_citation":"https://pith.science/pith/4TUW4LIPOIAFTFFFH5OSZDKNJL/action/citation_signature","submit_replication":"https://pith.science/pith/4TUW4LIPOIAFTFFFH5OSZDKNJL/action/replication_record"}},"created_at":"2026-07-05T06:14:56.255440+00:00","updated_at":"2026-07-05T06:14:56.255440+00:00"}