{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EH5RB2NVD6YEHIFUUWRGHP3LII","short_pith_number":"pith:EH5RB2NV","schema_version":"1.0","canonical_sha256":"21fb10e9b51fb043a0b4a5a263bf6b4203b260817150d9c4a1e10b3a648ef609","source":{"kind":"arxiv","id":"2303.05071","version":1},"attestation_state":"computed","paper":{"title":"MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Song-Hai Zhang, Tian-Xing Xu, Yuan-Chen Guo, Yu-Kun Lai","submitted_at":"2023-03-09T07:07:39Z","abstract_excerpt":"3D single object tracking has been a crucial problem for decades with numerous applications such as autonomous driving. Despite its wide-ranging use, this task remains challenging due to the significant appearance variation caused by occlusion and size differences among tracked targets. To address these issues, we present MBPTrack, which adopts a Memory mechanism to utilize past information and formulates localization in a coarse-to-fine scheme using Box Priors given in the first frame. Specifically, past frames with targetness masks serve as an external memory, and a transformer-based module "},"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":"2303.05071","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-09T07:07:39Z","cross_cats_sorted":[],"title_canon_sha256":"6c3d84e92f3ccbbbb9a0bba1f1c67a67e723c74fdec776da97dc8012e42513b8","abstract_canon_sha256":"c20d538fa0e73ab25a7910e3a78138c0a99e5db4cbfab3cacdd297282293416b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:36.989900Z","signature_b64":"Moo19pz4MmPMqQI1AoCTg0ONlTIWYSIRHA5dOY1eoW5RseqCxW1olzBa9wTuyN4QAGk8xyBqb7YoAPhVenMyDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21fb10e9b51fb043a0b4a5a263bf6b4203b260817150d9c4a1e10b3a648ef609","last_reissued_at":"2026-07-05T05:49:36.989478Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:36.989478Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Song-Hai Zhang, Tian-Xing Xu, Yuan-Chen Guo, Yu-Kun Lai","submitted_at":"2023-03-09T07:07:39Z","abstract_excerpt":"3D single object tracking has been a crucial problem for decades with numerous applications such as autonomous driving. Despite its wide-ranging use, this task remains challenging due to the significant appearance variation caused by occlusion and size differences among tracked targets. To address these issues, we present MBPTrack, which adopts a Memory mechanism to utilize past information and formulates localization in a coarse-to-fine scheme using Box Priors given in the first frame. Specifically, past frames with targetness masks serve as an external memory, and a transformer-based module "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.05071","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/2303.05071/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":"2303.05071","created_at":"2026-07-05T05:49:36.989536+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.05071v1","created_at":"2026-07-05T05:49:36.989536+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.05071","created_at":"2026-07-05T05:49:36.989536+00:00"},{"alias_kind":"pith_short_12","alias_value":"EH5RB2NVD6YE","created_at":"2026-07-05T05:49:36.989536+00:00"},{"alias_kind":"pith_short_16","alias_value":"EH5RB2NVD6YEHIFU","created_at":"2026-07-05T05:49:36.989536+00:00"},{"alias_kind":"pith_short_8","alias_value":"EH5RB2NV","created_at":"2026-07-05T05:49:36.989536+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.02734","citing_title":"MVCTrack: Boosting 3D Point Cloud Tracking via Multimodal-Guided Virtual Cues","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EH5RB2NVD6YEHIFUUWRGHP3LII","json":"https://pith.science/pith/EH5RB2NVD6YEHIFUUWRGHP3LII.json","graph_json":"https://pith.science/api/pith-number/EH5RB2NVD6YEHIFUUWRGHP3LII/graph.json","events_json":"https://pith.science/api/pith-number/EH5RB2NVD6YEHIFUUWRGHP3LII/events.json","paper":"https://pith.science/paper/EH5RB2NV"},"agent_actions":{"view_html":"https://pith.science/pith/EH5RB2NVD6YEHIFUUWRGHP3LII","download_json":"https://pith.science/pith/EH5RB2NVD6YEHIFUUWRGHP3LII.json","view_paper":"https://pith.science/paper/EH5RB2NV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.05071&json=true","fetch_graph":"https://pith.science/api/pith-number/EH5RB2NVD6YEHIFUUWRGHP3LII/graph.json","fetch_events":"https://pith.science/api/pith-number/EH5RB2NVD6YEHIFUUWRGHP3LII/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EH5RB2NVD6YEHIFUUWRGHP3LII/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EH5RB2NVD6YEHIFUUWRGHP3LII/action/storage_attestation","attest_author":"https://pith.science/pith/EH5RB2NVD6YEHIFUUWRGHP3LII/action/author_attestation","sign_citation":"https://pith.science/pith/EH5RB2NVD6YEHIFUUWRGHP3LII/action/citation_signature","submit_replication":"https://pith.science/pith/EH5RB2NVD6YEHIFUUWRGHP3LII/action/replication_record"}},"created_at":"2026-07-05T05:49:36.989536+00:00","updated_at":"2026-07-05T05:49:36.989536+00:00"}