{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:AJPTFEBEJHGX2LOWZ7BJOVHUYC","short_pith_number":"pith:AJPTFEBE","schema_version":"1.0","canonical_sha256":"025f32902449cd7d2dd6cfc29754f4c08751bec1c0ed0b7bb040149a314b86ca","source":{"kind":"arxiv","id":"2012.10660","version":1},"attestation_state":"computed","paper":{"title":"The importance of silhouette optimization in 3D shape reconstruction system from multiple object scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Martin Servin, Waqqas-ur-Rehman Butt","submitted_at":"2020-12-19T11:16:57Z","abstract_excerpt":"This paper presents a multi stage 3D shape reconstruction system of multiple object scenes by considering the silhouette inconsistencies in shape-from silhouette SFS method. These inconsistencies are common in multiple view images due to object occlusions in different views, segmentation and shadows or reflection due to objects or light directions. These factors raise huge challenges when attempting to construct the 3D shape by using existing approaches which reconstruct only that part of the volume which projects consistently in all the silhouettes, leaving the rest unreconstructed. As a resu"},"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":"2012.10660","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-19T11:16:57Z","cross_cats_sorted":[],"title_canon_sha256":"8a48aaedee06b8efa922184083af97ee31be4c48792645c2e6e0ff954eeb3e18","abstract_canon_sha256":"ecb878057bd975dcd22ad578c0ce1802a4f8bd98a628a75b4930932a6daddb09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:00:54.117776Z","signature_b64":"aoGZD0TMI26hSu4BIYMsnsafS5WWYRvyErmf6TfZlVsySI/pzaiw5aqSYcYx+LGty1a38qIcNZkD/c4F8tXLAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"025f32902449cd7d2dd6cfc29754f4c08751bec1c0ed0b7bb040149a314b86ca","last_reissued_at":"2026-07-05T02:00:54.117421Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:00:54.117421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The importance of silhouette optimization in 3D shape reconstruction system from multiple object scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Martin Servin, Waqqas-ur-Rehman Butt","submitted_at":"2020-12-19T11:16:57Z","abstract_excerpt":"This paper presents a multi stage 3D shape reconstruction system of multiple object scenes by considering the silhouette inconsistencies in shape-from silhouette SFS method. These inconsistencies are common in multiple view images due to object occlusions in different views, segmentation and shadows or reflection due to objects or light directions. These factors raise huge challenges when attempting to construct the 3D shape by using existing approaches which reconstruct only that part of the volume which projects consistently in all the silhouettes, leaving the rest unreconstructed. As a resu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.10660","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/2012.10660/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":"2012.10660","created_at":"2026-07-05T02:00:54.117477+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.10660v1","created_at":"2026-07-05T02:00:54.117477+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.10660","created_at":"2026-07-05T02:00:54.117477+00:00"},{"alias_kind":"pith_short_12","alias_value":"AJPTFEBEJHGX","created_at":"2026-07-05T02:00:54.117477+00:00"},{"alias_kind":"pith_short_16","alias_value":"AJPTFEBEJHGX2LOW","created_at":"2026-07-05T02:00:54.117477+00:00"},{"alias_kind":"pith_short_8","alias_value":"AJPTFEBE","created_at":"2026-07-05T02:00:54.117477+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.19759","citing_title":"A Machine Learning Framework for Predicting Microphysical Properties of Ice Crystals from Cloud Particle Imagery","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AJPTFEBEJHGX2LOWZ7BJOVHUYC","json":"https://pith.science/pith/AJPTFEBEJHGX2LOWZ7BJOVHUYC.json","graph_json":"https://pith.science/api/pith-number/AJPTFEBEJHGX2LOWZ7BJOVHUYC/graph.json","events_json":"https://pith.science/api/pith-number/AJPTFEBEJHGX2LOWZ7BJOVHUYC/events.json","paper":"https://pith.science/paper/AJPTFEBE"},"agent_actions":{"view_html":"https://pith.science/pith/AJPTFEBEJHGX2LOWZ7BJOVHUYC","download_json":"https://pith.science/pith/AJPTFEBEJHGX2LOWZ7BJOVHUYC.json","view_paper":"https://pith.science/paper/AJPTFEBE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.10660&json=true","fetch_graph":"https://pith.science/api/pith-number/AJPTFEBEJHGX2LOWZ7BJOVHUYC/graph.json","fetch_events":"https://pith.science/api/pith-number/AJPTFEBEJHGX2LOWZ7BJOVHUYC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AJPTFEBEJHGX2LOWZ7BJOVHUYC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AJPTFEBEJHGX2LOWZ7BJOVHUYC/action/storage_attestation","attest_author":"https://pith.science/pith/AJPTFEBEJHGX2LOWZ7BJOVHUYC/action/author_attestation","sign_citation":"https://pith.science/pith/AJPTFEBEJHGX2LOWZ7BJOVHUYC/action/citation_signature","submit_replication":"https://pith.science/pith/AJPTFEBEJHGX2LOWZ7BJOVHUYC/action/replication_record"}},"created_at":"2026-07-05T02:00:54.117477+00:00","updated_at":"2026-07-05T02:00:54.117477+00:00"}