{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:47NCK3OYRGO3PNHU3SE2RXFDYQ","short_pith_number":"pith:47NCK3OY","schema_version":"1.0","canonical_sha256":"e7da256dd8899db7b4f4dc89a8dca3c43559169c304c75b2a038e2187569e30a","source":{"kind":"arxiv","id":"1703.04699","version":1},"attestation_state":"computed","paper":{"title":"A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng Zhao, Li Sun, Rustam Stolkin","submitted_at":"2017-03-14T20:23:48Z","abstract_excerpt":"This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction has predominantly focused on recognition of everyday domestic objects (tables, chairs etc.), whereas previous work on material recognition has largely been confined to single 2D images without any 3D reconstruction. Meanwhile, most 3D semantic reconstruction methods rely on comp"},"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":"1703.04699","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-14T20:23:48Z","cross_cats_sorted":[],"title_canon_sha256":"b62abd31906bdcea09286fe3552f426ad574258e2939b74980de602d5488add2","abstract_canon_sha256":"f80c1eea99947b131abd74a773bbaed0a44f4ab1591f794a5e680299fa0fb545"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:47.864423Z","signature_b64":"EOS/6zpp7TUyKU7uRGYhs0rKpxmstl2QNUpjAB8VK9YsN5M45pqzazGwhol8ZIGr+AfnZ4nYoxMHYAmnkAMSAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7da256dd8899db7b4f4dc89a8dca3c43559169c304c75b2a038e2187569e30a","last_reissued_at":"2026-05-18T00:10:47.863720Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:47.863720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng Zhao, Li Sun, Rustam Stolkin","submitted_at":"2017-03-14T20:23:48Z","abstract_excerpt":"This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction has predominantly focused on recognition of everyday domestic objects (tables, chairs etc.), whereas previous work on material recognition has largely been confined to single 2D images without any 3D reconstruction. Meanwhile, most 3D semantic reconstruction methods rely on comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.04699","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":"1703.04699","created_at":"2026-05-18T00:10:47.863820+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.04699v1","created_at":"2026-05-18T00:10:47.863820+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.04699","created_at":"2026-05-18T00:10:47.863820+00:00"},{"alias_kind":"pith_short_12","alias_value":"47NCK3OYRGO3","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"47NCK3OYRGO3PNHU","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"47NCK3OY","created_at":"2026-05-18T12:30:58.224056+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/47NCK3OYRGO3PNHU3SE2RXFDYQ","json":"https://pith.science/pith/47NCK3OYRGO3PNHU3SE2RXFDYQ.json","graph_json":"https://pith.science/api/pith-number/47NCK3OYRGO3PNHU3SE2RXFDYQ/graph.json","events_json":"https://pith.science/api/pith-number/47NCK3OYRGO3PNHU3SE2RXFDYQ/events.json","paper":"https://pith.science/paper/47NCK3OY"},"agent_actions":{"view_html":"https://pith.science/pith/47NCK3OYRGO3PNHU3SE2RXFDYQ","download_json":"https://pith.science/pith/47NCK3OYRGO3PNHU3SE2RXFDYQ.json","view_paper":"https://pith.science/paper/47NCK3OY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.04699&json=true","fetch_graph":"https://pith.science/api/pith-number/47NCK3OYRGO3PNHU3SE2RXFDYQ/graph.json","fetch_events":"https://pith.science/api/pith-number/47NCK3OYRGO3PNHU3SE2RXFDYQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/47NCK3OYRGO3PNHU3SE2RXFDYQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/47NCK3OYRGO3PNHU3SE2RXFDYQ/action/storage_attestation","attest_author":"https://pith.science/pith/47NCK3OYRGO3PNHU3SE2RXFDYQ/action/author_attestation","sign_citation":"https://pith.science/pith/47NCK3OYRGO3PNHU3SE2RXFDYQ/action/citation_signature","submit_replication":"https://pith.science/pith/47NCK3OYRGO3PNHU3SE2RXFDYQ/action/replication_record"}},"created_at":"2026-05-18T00:10:47.863820+00:00","updated_at":"2026-05-18T00:10:47.863820+00:00"}