{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HE2BP27VKFZGXX2OWDGPY7UQHS","short_pith_number":"pith:HE2BP27V","schema_version":"1.0","canonical_sha256":"393417ebf551726bdf4eb0ccfc7e903c87e3ecefcc66c3f9d6db9b5c9f0b3281","source":{"kind":"arxiv","id":"2503.11979","version":1},"attestation_state":"computed","paper":{"title":"DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, Motion Predictions of Moving Objects in Dynamic Scenes","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bang Du, Keito Suzuki, Ki Myung Brian Lee, Mahdi Shaghaghi, Martin Renschler, Nikolay Atanasov, Runfa Blark Li, Truong Nguyen, Varun Moparthi, Walker Curtis, Xinshuang Liu","submitted_at":"2025-03-15T03:20:14Z","abstract_excerpt":"Simultaneous Localization and Mapping (SLAM) is one of the most important environment-perception and navigation algorithms for computer vision, robotics, and autonomous cars/drones. Hence, high quality and fast mapping becomes a fundamental problem. With the advent of 3D Gaussian Splatting (3DGS) as an explicit representation with excellent rendering quality and speed, state-of-the-art (SOTA) works introduce GS to SLAM. Compared to classical pointcloud-SLAM, GS-SLAM generates photometric information by learning from input camera views and synthesize unseen views with high-quality textures. How"},"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":"2503.11979","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-15T03:20:14Z","cross_cats_sorted":[],"title_canon_sha256":"c82fe7d925e3d94d8b88e7198e86576d8c3f4c423770c271aa63386275e44c9b","abstract_canon_sha256":"b7de07d4530e6a21ccdef520893020064d532e65835ae2b2e92c9fe35390b2e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:03.043430Z","signature_b64":"oeb/5M9Gc8H8AHgJi6yUCLAEji7Hdprt9a1VWkqU15eOx3foqD8BemSBKmorzBoaLch0GmCcEeHhOroig5P0Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"393417ebf551726bdf4eb0ccfc7e903c87e3ecefcc66c3f9d6db9b5c9f0b3281","last_reissued_at":"2026-07-05T10:32:03.042921Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:03.042921Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, Motion Predictions of Moving Objects in Dynamic Scenes","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bang Du, Keito Suzuki, Ki Myung Brian Lee, Mahdi Shaghaghi, Martin Renschler, Nikolay Atanasov, Runfa Blark Li, Truong Nguyen, Varun Moparthi, Walker Curtis, Xinshuang Liu","submitted_at":"2025-03-15T03:20:14Z","abstract_excerpt":"Simultaneous Localization and Mapping (SLAM) is one of the most important environment-perception and navigation algorithms for computer vision, robotics, and autonomous cars/drones. Hence, high quality and fast mapping becomes a fundamental problem. With the advent of 3D Gaussian Splatting (3DGS) as an explicit representation with excellent rendering quality and speed, state-of-the-art (SOTA) works introduce GS to SLAM. Compared to classical pointcloud-SLAM, GS-SLAM generates photometric information by learning from input camera views and synthesize unseen views with high-quality textures. How"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.11979","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/2503.11979/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":"2503.11979","created_at":"2026-07-05T10:32:03.042981+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.11979v1","created_at":"2026-07-05T10:32:03.042981+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.11979","created_at":"2026-07-05T10:32:03.042981+00:00"},{"alias_kind":"pith_short_12","alias_value":"HE2BP27VKFZG","created_at":"2026-07-05T10:32:03.042981+00:00"},{"alias_kind":"pith_short_16","alias_value":"HE2BP27VKFZGXX2O","created_at":"2026-07-05T10:32:03.042981+00:00"},{"alias_kind":"pith_short_8","alias_value":"HE2BP27V","created_at":"2026-07-05T10:32:03.042981+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02759","citing_title":"DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2602.14199","citing_title":"Learnable Multi-level Discrete Wavelet Transforms for 3D Gaussian Splatting Frequency Modulation","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02696","citing_title":"VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02759","citing_title":"DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HE2BP27VKFZGXX2OWDGPY7UQHS","json":"https://pith.science/pith/HE2BP27VKFZGXX2OWDGPY7UQHS.json","graph_json":"https://pith.science/api/pith-number/HE2BP27VKFZGXX2OWDGPY7UQHS/graph.json","events_json":"https://pith.science/api/pith-number/HE2BP27VKFZGXX2OWDGPY7UQHS/events.json","paper":"https://pith.science/paper/HE2BP27V"},"agent_actions":{"view_html":"https://pith.science/pith/HE2BP27VKFZGXX2OWDGPY7UQHS","download_json":"https://pith.science/pith/HE2BP27VKFZGXX2OWDGPY7UQHS.json","view_paper":"https://pith.science/paper/HE2BP27V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.11979&json=true","fetch_graph":"https://pith.science/api/pith-number/HE2BP27VKFZGXX2OWDGPY7UQHS/graph.json","fetch_events":"https://pith.science/api/pith-number/HE2BP27VKFZGXX2OWDGPY7UQHS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HE2BP27VKFZGXX2OWDGPY7UQHS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HE2BP27VKFZGXX2OWDGPY7UQHS/action/storage_attestation","attest_author":"https://pith.science/pith/HE2BP27VKFZGXX2OWDGPY7UQHS/action/author_attestation","sign_citation":"https://pith.science/pith/HE2BP27VKFZGXX2OWDGPY7UQHS/action/citation_signature","submit_replication":"https://pith.science/pith/HE2BP27VKFZGXX2OWDGPY7UQHS/action/replication_record"}},"created_at":"2026-07-05T10:32:03.042981+00:00","updated_at":"2026-07-05T10:32:03.042981+00:00"}