{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:VH6S5O3OQKWF6SBIZTXPLDQGXK","short_pith_number":"pith:VH6S5O3O","schema_version":"1.0","canonical_sha256":"a9fd2ebb6e82ac5f4828cceef58e06ba9c8c4c2268329868e2c805b8deca7da3","source":{"kind":"arxiv","id":"1610.05883","version":1},"attestation_state":"computed","paper":{"title":"A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Binh-Son Hua, Duc Thanh Nguyen, Lap-Fai Yu, Sai-Kit Yeung","submitted_at":"2016-10-19T06:54:02Z","abstract_excerpt":"Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data. Such data, if completely and well annotated, can serve as useful ingredients for a wide spectrum of computer vision and graphics works such as data-driven modeling and scene understanding, object detection and recognition. However, annotating a vast amount of 3D scene data remains challenging due to the lack of an effective tool and/or the complexity of 3D scenes (e.g. clutter, varying illumination conditions). This paper aims to build a robust annotation tool that effectively and conveniently enab"},"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":"1610.05883","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-19T06:54:02Z","cross_cats_sorted":[],"title_canon_sha256":"b903ec256425033ce9f645252b5c789b6db38c38538a8b9fd4fa602358b3400b","abstract_canon_sha256":"9cfa8db5844018581ce5fc4fc96c4e14e5d100710f21a8e1ecd25783672771b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:01:50.830911Z","signature_b64":"iS/7WdyHQgiAYF5NMHrBfH/dnpenXCinPni+AeMq4d9M6LtXcVMtplmoPqZwjTNJ/bDJ80xjXa4E4QwELuF6DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9fd2ebb6e82ac5f4828cceef58e06ba9c8c4c2268329868e2c805b8deca7da3","last_reissued_at":"2026-05-18T01:01:50.830165Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:01:50.830165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Binh-Son Hua, Duc Thanh Nguyen, Lap-Fai Yu, Sai-Kit Yeung","submitted_at":"2016-10-19T06:54:02Z","abstract_excerpt":"Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data. Such data, if completely and well annotated, can serve as useful ingredients for a wide spectrum of computer vision and graphics works such as data-driven modeling and scene understanding, object detection and recognition. However, annotating a vast amount of 3D scene data remains challenging due to the lack of an effective tool and/or the complexity of 3D scenes (e.g. clutter, varying illumination conditions). This paper aims to build a robust annotation tool that effectively and conveniently enab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.05883","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":"1610.05883","created_at":"2026-05-18T01:01:50.830284+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.05883v1","created_at":"2026-05-18T01:01:50.830284+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.05883","created_at":"2026-05-18T01:01:50.830284+00:00"},{"alias_kind":"pith_short_12","alias_value":"VH6S5O3OQKWF","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"VH6S5O3OQKWF6SBI","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"VH6S5O3O","created_at":"2026-05-18T12:30:48.956258+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.01862","citing_title":"Semi-Automatic Labeling for Deep Learning in Robotics","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VH6S5O3OQKWF6SBIZTXPLDQGXK","json":"https://pith.science/pith/VH6S5O3OQKWF6SBIZTXPLDQGXK.json","graph_json":"https://pith.science/api/pith-number/VH6S5O3OQKWF6SBIZTXPLDQGXK/graph.json","events_json":"https://pith.science/api/pith-number/VH6S5O3OQKWF6SBIZTXPLDQGXK/events.json","paper":"https://pith.science/paper/VH6S5O3O"},"agent_actions":{"view_html":"https://pith.science/pith/VH6S5O3OQKWF6SBIZTXPLDQGXK","download_json":"https://pith.science/pith/VH6S5O3OQKWF6SBIZTXPLDQGXK.json","view_paper":"https://pith.science/paper/VH6S5O3O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.05883&json=true","fetch_graph":"https://pith.science/api/pith-number/VH6S5O3OQKWF6SBIZTXPLDQGXK/graph.json","fetch_events":"https://pith.science/api/pith-number/VH6S5O3OQKWF6SBIZTXPLDQGXK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VH6S5O3OQKWF6SBIZTXPLDQGXK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VH6S5O3OQKWF6SBIZTXPLDQGXK/action/storage_attestation","attest_author":"https://pith.science/pith/VH6S5O3OQKWF6SBIZTXPLDQGXK/action/author_attestation","sign_citation":"https://pith.science/pith/VH6S5O3OQKWF6SBIZTXPLDQGXK/action/citation_signature","submit_replication":"https://pith.science/pith/VH6S5O3OQKWF6SBIZTXPLDQGXK/action/replication_record"}},"created_at":"2026-05-18T01:01:50.830284+00:00","updated_at":"2026-05-18T01:01:50.830284+00:00"}