{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:Y7ILES3IAUWARLUAV6NZFGRBTD","short_pith_number":"pith:Y7ILES3I","schema_version":"1.0","canonical_sha256":"c7d0b24b68052c08ae80af9b929a2198c858d2cadd611d29738caa9bbd44347f","source":{"kind":"arxiv","id":"1711.06363","version":2},"attestation_state":"computed","paper":{"title":"3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ivan Sipiran, Renato Hermoza","submitted_at":"2017-11-17T00:58:53Z","abstract_excerpt":"We introduce a data-driven approach to aid the repairing and conservation of archaeological objects: ORGAN, an object reconstruction generative adversarial network (GAN). By using an encoder-decoder 3D deep neural network on a GAN architecture, and combining two loss objectives: a completion loss and an Improved Wasserstein GAN loss, we can train a network to effectively predict the missing geometry of damaged objects. As archaeological objects can greatly differ between them, the network is conditioned on a variable, which can be a culture, a region or any metadata of the object. In our resul"},"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":"1711.06363","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-17T00:58:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f4f2c1f077cdbfa1c5f80ba3bef583723f776c205ba92c8a28b18116ab4ef7af","abstract_canon_sha256":"30017b68867a74e444e11522f06ca85402657066a49d0b91ab151ef3db17d439"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:34.515752Z","signature_b64":"NdrVyiY7Ebpb+9eO/m661+zn+++90aYTG54BPKMc3NEVygMQp6wJua+qafubsYiEFr8bD8jdCDenjmfPmLa9DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7d0b24b68052c08ae80af9b929a2198c858d2cadd611d29738caa9bbd44347f","last_reissued_at":"2026-05-18T00:21:34.515163Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:34.515163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ivan Sipiran, Renato Hermoza","submitted_at":"2017-11-17T00:58:53Z","abstract_excerpt":"We introduce a data-driven approach to aid the repairing and conservation of archaeological objects: ORGAN, an object reconstruction generative adversarial network (GAN). By using an encoder-decoder 3D deep neural network on a GAN architecture, and combining two loss objectives: a completion loss and an Improved Wasserstein GAN loss, we can train a network to effectively predict the missing geometry of damaged objects. As archaeological objects can greatly differ between them, the network is conditioned on a variable, which can be a culture, a region or any metadata of the object. In our resul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.06363","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":""},"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":"1711.06363","created_at":"2026-05-18T00:21:34.515252+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.06363v2","created_at":"2026-05-18T00:21:34.515252+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.06363","created_at":"2026-05-18T00:21:34.515252+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y7ILES3IAUWA","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y7ILES3IAUWARLUA","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y7ILES3I","created_at":"2026-05-18T12:31:56.362134+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/Y7ILES3IAUWARLUAV6NZFGRBTD","json":"https://pith.science/pith/Y7ILES3IAUWARLUAV6NZFGRBTD.json","graph_json":"https://pith.science/api/pith-number/Y7ILES3IAUWARLUAV6NZFGRBTD/graph.json","events_json":"https://pith.science/api/pith-number/Y7ILES3IAUWARLUAV6NZFGRBTD/events.json","paper":"https://pith.science/paper/Y7ILES3I"},"agent_actions":{"view_html":"https://pith.science/pith/Y7ILES3IAUWARLUAV6NZFGRBTD","download_json":"https://pith.science/pith/Y7ILES3IAUWARLUAV6NZFGRBTD.json","view_paper":"https://pith.science/paper/Y7ILES3I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.06363&json=true","fetch_graph":"https://pith.science/api/pith-number/Y7ILES3IAUWARLUAV6NZFGRBTD/graph.json","fetch_events":"https://pith.science/api/pith-number/Y7ILES3IAUWARLUAV6NZFGRBTD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y7ILES3IAUWARLUAV6NZFGRBTD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y7ILES3IAUWARLUAV6NZFGRBTD/action/storage_attestation","attest_author":"https://pith.science/pith/Y7ILES3IAUWARLUAV6NZFGRBTD/action/author_attestation","sign_citation":"https://pith.science/pith/Y7ILES3IAUWARLUAV6NZFGRBTD/action/citation_signature","submit_replication":"https://pith.science/pith/Y7ILES3IAUWARLUAV6NZFGRBTD/action/replication_record"}},"created_at":"2026-05-18T00:21:34.515252+00:00","updated_at":"2026-05-18T00:21:34.515252+00:00"}