{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JEBKKLXLKRJPC5P7OP7NKLMTU7","short_pith_number":"pith:JEBKKLXL","schema_version":"1.0","canonical_sha256":"4902a52eeb5452f175ff73fed52d93a7dbbc13d649ad922470b4eeb8b6596c85","source":{"kind":"arxiv","id":"2310.18609","version":1},"attestation_state":"computed","paper":{"title":"Deep3DSketch+: Obtaining Customized 3D Model by Single Free-Hand Sketch through Deep Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Chenglong Fu, Qingshan Liu, Tianrun Chen, Wenjun Hu, Ying Zang, Yuanqi Hu","submitted_at":"2023-10-28T06:36:53Z","abstract_excerpt":"As 3D models become critical in today's manufacturing and product design, conventional 3D modeling approaches based on Computer-Aided Design (CAD) are labor-intensive, time-consuming, and have high demands on the creators. This work aims to introduce an alternative approach to 3D modeling by utilizing free-hand sketches to obtain desired 3D models. We introduce Deep3DSketch+, which is a deep-learning algorithm that takes the input of a single free-hand sketch and produces a complete and high-fidelity model that matches the sketch input. The neural network has view- and structural-awareness ena"},"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":"2310.18609","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.MM","submitted_at":"2023-10-28T06:36:53Z","cross_cats_sorted":[],"title_canon_sha256":"3b32c46f419c203c64eb5f4d784b0c9f816d4622abbe6a1956f60fc6853d45cb","abstract_canon_sha256":"2d1cad4e9f8fea1464df9b836b1c8e2d687738519fc93c59458497a89d98314d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:06:11.075684Z","signature_b64":"hAeidtUY/pRBV7vQFerfN3x0FOiPaoGD1A2xBDKW4h1fUy5Wfp5XsafL0r0es2T+LpthIs/ta/vCHbWwrj+MAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4902a52eeb5452f175ff73fed52d93a7dbbc13d649ad922470b4eeb8b6596c85","last_reissued_at":"2026-07-05T07:06:11.075196Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:06:11.075196Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep3DSketch+: Obtaining Customized 3D Model by Single Free-Hand Sketch through Deep Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Chenglong Fu, Qingshan Liu, Tianrun Chen, Wenjun Hu, Ying Zang, Yuanqi Hu","submitted_at":"2023-10-28T06:36:53Z","abstract_excerpt":"As 3D models become critical in today's manufacturing and product design, conventional 3D modeling approaches based on Computer-Aided Design (CAD) are labor-intensive, time-consuming, and have high demands on the creators. This work aims to introduce an alternative approach to 3D modeling by utilizing free-hand sketches to obtain desired 3D models. We introduce Deep3DSketch+, which is a deep-learning algorithm that takes the input of a single free-hand sketch and produces a complete and high-fidelity model that matches the sketch input. The neural network has view- and structural-awareness ena"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.18609","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.18609/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":"2310.18609","created_at":"2026-07-05T07:06:11.075249+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.18609v1","created_at":"2026-07-05T07:06:11.075249+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.18609","created_at":"2026-07-05T07:06:11.075249+00:00"},{"alias_kind":"pith_short_12","alias_value":"JEBKKLXLKRJP","created_at":"2026-07-05T07:06:11.075249+00:00"},{"alias_kind":"pith_short_16","alias_value":"JEBKKLXLKRJPC5P7","created_at":"2026-07-05T07:06:11.075249+00:00"},{"alias_kind":"pith_short_8","alias_value":"JEBKKLXL","created_at":"2026-07-05T07:06:11.075249+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.19329","citing_title":"Let Human Sketches Help: Empowering Challenging Image Segmentation Task with Freehand Sketches","ref_index":2018,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JEBKKLXLKRJPC5P7OP7NKLMTU7","json":"https://pith.science/pith/JEBKKLXLKRJPC5P7OP7NKLMTU7.json","graph_json":"https://pith.science/api/pith-number/JEBKKLXLKRJPC5P7OP7NKLMTU7/graph.json","events_json":"https://pith.science/api/pith-number/JEBKKLXLKRJPC5P7OP7NKLMTU7/events.json","paper":"https://pith.science/paper/JEBKKLXL"},"agent_actions":{"view_html":"https://pith.science/pith/JEBKKLXLKRJPC5P7OP7NKLMTU7","download_json":"https://pith.science/pith/JEBKKLXLKRJPC5P7OP7NKLMTU7.json","view_paper":"https://pith.science/paper/JEBKKLXL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.18609&json=true","fetch_graph":"https://pith.science/api/pith-number/JEBKKLXLKRJPC5P7OP7NKLMTU7/graph.json","fetch_events":"https://pith.science/api/pith-number/JEBKKLXLKRJPC5P7OP7NKLMTU7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JEBKKLXLKRJPC5P7OP7NKLMTU7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JEBKKLXLKRJPC5P7OP7NKLMTU7/action/storage_attestation","attest_author":"https://pith.science/pith/JEBKKLXLKRJPC5P7OP7NKLMTU7/action/author_attestation","sign_citation":"https://pith.science/pith/JEBKKLXLKRJPC5P7OP7NKLMTU7/action/citation_signature","submit_replication":"https://pith.science/pith/JEBKKLXLKRJPC5P7OP7NKLMTU7/action/replication_record"}},"created_at":"2026-07-05T07:06:11.075249+00:00","updated_at":"2026-07-05T07:06:11.075249+00:00"}