{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2K7UI3LP7PAHGTPMYAJIF56AJY","short_pith_number":"pith:2K7UI3LP","schema_version":"1.0","canonical_sha256":"d2bf446d6ffbc0734decc01282f7c04e1f0ab4ead0a786526970d145f560760a","source":{"kind":"arxiv","id":"2404.05522","version":2},"attestation_state":"computed","paper":{"title":"3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Ben Fei, Jingyi Xu, Keyi Liu, Qingyuan Zhou, Rui Zhang, Weidong Yang, Yeqi Luo, Ying He","submitted_at":"2024-04-08T13:43:19Z","abstract_excerpt":"Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on small-scale synthetic or real-world datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quantity of point clouds. This limitation primarily stems from their limited denoising capabilities for large-scale point clouds and their inclination to generate noisy outliers after denoising. The recent introduction of State Space Models (SSM"},"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":"2404.05522","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2024-04-08T13:43:19Z","cross_cats_sorted":[],"title_canon_sha256":"2454fc88e66cfdeecd8e51402a62355a6e5799f938853813b3fa598bb193e68e","abstract_canon_sha256":"432db6e1c0a3b60081c5974d1a016d6c11023eb26a128b245d23d0dc29a4e2ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:41.142816Z","signature_b64":"7/IUUieseXavSirwF4H/1TO4XrnvGtpEwPtEk/J5Urmowfa69J3SK5KpG/ptQiIe+k8oqhq96LYK6yqZKe00Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2bf446d6ffbc0734decc01282f7c04e1f0ab4ead0a786526970d145f560760a","last_reissued_at":"2026-07-05T09:58:41.142307Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:41.142307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Ben Fei, Jingyi Xu, Keyi Liu, Qingyuan Zhou, Rui Zhang, Weidong Yang, Yeqi Luo, Ying He","submitted_at":"2024-04-08T13:43:19Z","abstract_excerpt":"Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on small-scale synthetic or real-world datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quantity of point clouds. This limitation primarily stems from their limited denoising capabilities for large-scale point clouds and their inclination to generate noisy outliers after denoising. The recent introduction of State Space Models (SSM"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.05522","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/2404.05522/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":"2404.05522","created_at":"2026-07-05T09:58:41.142386+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.05522v2","created_at":"2026-07-05T09:58:41.142386+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.05522","created_at":"2026-07-05T09:58:41.142386+00:00"},{"alias_kind":"pith_short_12","alias_value":"2K7UI3LP7PAH","created_at":"2026-07-05T09:58:41.142386+00:00"},{"alias_kind":"pith_short_16","alias_value":"2K7UI3LP7PAHGTPM","created_at":"2026-07-05T09:58:41.142386+00:00"},{"alias_kind":"pith_short_8","alias_value":"2K7UI3LP","created_at":"2026-07-05T09:58:41.142386+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2408.01129","citing_title":"A Survey of Mamba","ref_index":244,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2K7UI3LP7PAHGTPMYAJIF56AJY","json":"https://pith.science/pith/2K7UI3LP7PAHGTPMYAJIF56AJY.json","graph_json":"https://pith.science/api/pith-number/2K7UI3LP7PAHGTPMYAJIF56AJY/graph.json","events_json":"https://pith.science/api/pith-number/2K7UI3LP7PAHGTPMYAJIF56AJY/events.json","paper":"https://pith.science/paper/2K7UI3LP"},"agent_actions":{"view_html":"https://pith.science/pith/2K7UI3LP7PAHGTPMYAJIF56AJY","download_json":"https://pith.science/pith/2K7UI3LP7PAHGTPMYAJIF56AJY.json","view_paper":"https://pith.science/paper/2K7UI3LP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.05522&json=true","fetch_graph":"https://pith.science/api/pith-number/2K7UI3LP7PAHGTPMYAJIF56AJY/graph.json","fetch_events":"https://pith.science/api/pith-number/2K7UI3LP7PAHGTPMYAJIF56AJY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2K7UI3LP7PAHGTPMYAJIF56AJY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2K7UI3LP7PAHGTPMYAJIF56AJY/action/storage_attestation","attest_author":"https://pith.science/pith/2K7UI3LP7PAHGTPMYAJIF56AJY/action/author_attestation","sign_citation":"https://pith.science/pith/2K7UI3LP7PAHGTPMYAJIF56AJY/action/citation_signature","submit_replication":"https://pith.science/pith/2K7UI3LP7PAHGTPMYAJIF56AJY/action/replication_record"}},"created_at":"2026-07-05T09:58:41.142386+00:00","updated_at":"2026-07-05T09:58:41.142386+00:00"}