{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AFL453UO6VYPTY72AVOZWMNHMK","short_pith_number":"pith:AFL453UO","schema_version":"1.0","canonical_sha256":"0157ceee8ef570f9e3fa055d9b31a7628b32945b1e15de397485f43081224514","source":{"kind":"arxiv","id":"2209.13274","version":2},"attestation_state":"computed","paper":{"title":"Orbeez-SLAM: A Real-time Monocular Visual SLAM with ORB Features and NeRF-realized Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Chi-Ming Chung, Jia-Fong Yeh, Wen-Chin Chen, Winston H. Hsu, Xiang-Qian Shi, Ya-Ching Hsu, Yang-Che Tseng, Yi-Ting Chen, Yun-Hung Hua","submitted_at":"2022-09-27T09:37:57Z","abstract_excerpt":"A spatial AI that can perform complex tasks through visual signals and cooperate with humans is highly anticipated. To achieve this, we need a visual SLAM that easily adapts to new scenes without pre-training and generates dense maps for downstream tasks in real-time. None of the previous learning-based and non-learning-based visual SLAMs satisfy all needs due to the intrinsic limitations of their components. In this work, we develop a visual SLAM named Orbeez-SLAM, which successfully collaborates with implicit neural representation and visual odometry to achieve our goals. Moreover, Orbeez-SL"},"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":"2209.13274","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2022-09-27T09:37:57Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"deb924bc3d3f3ee93f51be62d48cf966402aa4d0c520bb791c532e740acd1eaf","abstract_canon_sha256":"5b4a3b74f48eb2093dad352d58b7c954683be4f4fe7480f74e8afff3eefbd97f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:37:08.828477Z","signature_b64":"SV66unynD8EcdIbinqEeonPEDl/r45WdZXH0QDk+BRhdHnlqH0s7MWVH0rgXz396ureuOqeAFG1PwUatGx0EBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0157ceee8ef570f9e3fa055d9b31a7628b32945b1e15de397485f43081224514","last_reissued_at":"2026-07-05T05:37:08.828051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:37:08.828051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Orbeez-SLAM: A Real-time Monocular Visual SLAM with ORB Features and NeRF-realized Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Chi-Ming Chung, Jia-Fong Yeh, Wen-Chin Chen, Winston H. Hsu, Xiang-Qian Shi, Ya-Ching Hsu, Yang-Che Tseng, Yi-Ting Chen, Yun-Hung Hua","submitted_at":"2022-09-27T09:37:57Z","abstract_excerpt":"A spatial AI that can perform complex tasks through visual signals and cooperate with humans is highly anticipated. To achieve this, we need a visual SLAM that easily adapts to new scenes without pre-training and generates dense maps for downstream tasks in real-time. None of the previous learning-based and non-learning-based visual SLAMs satisfy all needs due to the intrinsic limitations of their components. In this work, we develop a visual SLAM named Orbeez-SLAM, which successfully collaborates with implicit neural representation and visual odometry to achieve our goals. Moreover, Orbeez-SL"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.13274","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2209.13274/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":"2209.13274","created_at":"2026-07-05T05:37:08.828114+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.13274v2","created_at":"2026-07-05T05:37:08.828114+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.13274","created_at":"2026-07-05T05:37:08.828114+00:00"},{"alias_kind":"pith_short_12","alias_value":"AFL453UO6VYP","created_at":"2026-07-05T05:37:08.828114+00:00"},{"alias_kind":"pith_short_16","alias_value":"AFL453UO6VYPTY72","created_at":"2026-07-05T05:37:08.828114+00:00"},{"alias_kind":"pith_short_8","alias_value":"AFL453UO","created_at":"2026-07-05T05:37:08.828114+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.19946","citing_title":"SL(C)AMma: Simultaneous Localisation, (Calibration) and Mapping With a Magnetometer Array","ref_index":71,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AFL453UO6VYPTY72AVOZWMNHMK","json":"https://pith.science/pith/AFL453UO6VYPTY72AVOZWMNHMK.json","graph_json":"https://pith.science/api/pith-number/AFL453UO6VYPTY72AVOZWMNHMK/graph.json","events_json":"https://pith.science/api/pith-number/AFL453UO6VYPTY72AVOZWMNHMK/events.json","paper":"https://pith.science/paper/AFL453UO"},"agent_actions":{"view_html":"https://pith.science/pith/AFL453UO6VYPTY72AVOZWMNHMK","download_json":"https://pith.science/pith/AFL453UO6VYPTY72AVOZWMNHMK.json","view_paper":"https://pith.science/paper/AFL453UO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.13274&json=true","fetch_graph":"https://pith.science/api/pith-number/AFL453UO6VYPTY72AVOZWMNHMK/graph.json","fetch_events":"https://pith.science/api/pith-number/AFL453UO6VYPTY72AVOZWMNHMK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AFL453UO6VYPTY72AVOZWMNHMK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AFL453UO6VYPTY72AVOZWMNHMK/action/storage_attestation","attest_author":"https://pith.science/pith/AFL453UO6VYPTY72AVOZWMNHMK/action/author_attestation","sign_citation":"https://pith.science/pith/AFL453UO6VYPTY72AVOZWMNHMK/action/citation_signature","submit_replication":"https://pith.science/pith/AFL453UO6VYPTY72AVOZWMNHMK/action/replication_record"}},"created_at":"2026-07-05T05:37:08.828114+00:00","updated_at":"2026-07-05T05:37:08.828114+00:00"}