{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YXAGZMSDTJ2S6HXNMY63MRBCRJ","short_pith_number":"pith:YXAGZMSD","schema_version":"1.0","canonical_sha256":"c5c06cb2439a752f1eed663db644228a58712bfaf2b6b83d3cd37c0bffe76927","source":{"kind":"arxiv","id":"2308.07118","version":2},"attestation_state":"computed","paper":{"title":"Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Enric Pardo, Eugen \\v{S}lapak, Juraj Gazda, Mat\\'u\\v{s} Dopiriak, Taras Maksymyuk","submitted_at":"2023-08-14T12:57:12Z","abstract_excerpt":"The proliferation of technologies, such as extended reality (XR), has increased the demand for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications encompass computer-aided design (CAD), finite element analysis (FEA), scanning, and robotics. However, current methods employed for industrial 3D representations suffer from high implementation costs and reliance on manual human input for accurate 3D modeling. To address these challenges, neural radiance fields (NeRFs) have emerged as a promising approach for learning 3D scene representations based on provided 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":"2308.07118","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-08-14T12:57:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"355e2aaa03bb7729480fcce43f48070e42c274cddbe798f49d4669d053f15094","abstract_canon_sha256":"cbda324be3fdb860a948136f5b0d7ea9a987dfebe81e041b4932142bf0f59f73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:08.117486Z","signature_b64":"G0/daX1divZ13pnJqT+EtO+wnMmy7uxSer0JhkjmNcew0jX3kl7bRfSV/owl+yTgUAa/sK9DAS2Ov2yqMyrvCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5c06cb2439a752f1eed663db644228a58712bfaf2b6b83d3cd37c0bffe76927","last_reissued_at":"2026-07-05T10:53:08.116993Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:08.116993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Enric Pardo, Eugen \\v{S}lapak, Juraj Gazda, Mat\\'u\\v{s} Dopiriak, Taras Maksymyuk","submitted_at":"2023-08-14T12:57:12Z","abstract_excerpt":"The proliferation of technologies, such as extended reality (XR), has increased the demand for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications encompass computer-aided design (CAD), finite element analysis (FEA), scanning, and robotics. However, current methods employed for industrial 3D representations suffer from high implementation costs and reliance on manual human input for accurate 3D modeling. To address these challenges, neural radiance fields (NeRFs) have emerged as a promising approach for learning 3D scene representations based on provided t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.07118","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/2308.07118/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":"2308.07118","created_at":"2026-07-05T10:53:08.117050+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.07118v2","created_at":"2026-07-05T10:53:08.117050+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.07118","created_at":"2026-07-05T10:53:08.117050+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXAGZMSDTJ2S","created_at":"2026-07-05T10:53:08.117050+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXAGZMSDTJ2S6HXN","created_at":"2026-07-05T10:53:08.117050+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXAGZMSD","created_at":"2026-07-05T10:53:08.117050+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.21030","citing_title":"Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YXAGZMSDTJ2S6HXNMY63MRBCRJ","json":"https://pith.science/pith/YXAGZMSDTJ2S6HXNMY63MRBCRJ.json","graph_json":"https://pith.science/api/pith-number/YXAGZMSDTJ2S6HXNMY63MRBCRJ/graph.json","events_json":"https://pith.science/api/pith-number/YXAGZMSDTJ2S6HXNMY63MRBCRJ/events.json","paper":"https://pith.science/paper/YXAGZMSD"},"agent_actions":{"view_html":"https://pith.science/pith/YXAGZMSDTJ2S6HXNMY63MRBCRJ","download_json":"https://pith.science/pith/YXAGZMSDTJ2S6HXNMY63MRBCRJ.json","view_paper":"https://pith.science/paper/YXAGZMSD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.07118&json=true","fetch_graph":"https://pith.science/api/pith-number/YXAGZMSDTJ2S6HXNMY63MRBCRJ/graph.json","fetch_events":"https://pith.science/api/pith-number/YXAGZMSDTJ2S6HXNMY63MRBCRJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXAGZMSDTJ2S6HXNMY63MRBCRJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXAGZMSDTJ2S6HXNMY63MRBCRJ/action/storage_attestation","attest_author":"https://pith.science/pith/YXAGZMSDTJ2S6HXNMY63MRBCRJ/action/author_attestation","sign_citation":"https://pith.science/pith/YXAGZMSDTJ2S6HXNMY63MRBCRJ/action/citation_signature","submit_replication":"https://pith.science/pith/YXAGZMSDTJ2S6HXNMY63MRBCRJ/action/replication_record"}},"created_at":"2026-07-05T10:53:08.117050+00:00","updated_at":"2026-07-05T10:53:08.117050+00:00"}