{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7EB5KF5EKHCJCRN6EOX4NDBI36","short_pith_number":"pith:7EB5KF5E","schema_version":"1.0","canonical_sha256":"f903d517a451c49145be23afc68c28df80c1937d3ba53c720e733c54d1b63f73","source":{"kind":"arxiv","id":"2411.08727","version":1},"attestation_state":"computed","paper":{"title":"Voxeland: Probabilistic Instance-Aware Semantic Mapping with Evidence-based Uncertainty Quantification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Javier Gonzalez-Jimenez, Javier Monroy, Jose-Luis Matez-Bandera, Jose-Raul Ruiz-Sarmiento, Pepe Ojeda","submitted_at":"2024-11-13T16:09:04Z","abstract_excerpt":"Robots in human-centered environments require accurate scene understanding to perform high-level tasks effectively. This understanding can be achieved through instance-aware semantic mapping, which involves reconstructing elements at the level of individual instances. Neural networks, the de facto solution for scene understanding, still face limitations such as overconfident incorrect predictions with out-of-distribution objects or generating inaccurate masks.Placing excessive reliance on these predictions makes the reconstruction susceptible to errors, reducing the robustness of the resulting"},"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":"2411.08727","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-11-13T16:09:04Z","cross_cats_sorted":[],"title_canon_sha256":"3c35d6cbed1be5bac1708ea384d1192054edc0716652189100c5675516e96460","abstract_canon_sha256":"cf3d718fe17e522e5323a35cbe7ae276bf6f8d1cd4e9c931eb4479753c9d380d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:35:01.614715Z","signature_b64":"ljfMQOQx5mRrkN0G/qj92pUJBzdZx3RweO9k/AkjJGHAlR6PmBV8dX9DgbQJT7joScgX/k9f5OsjPui1uLWQAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f903d517a451c49145be23afc68c28df80c1937d3ba53c720e733c54d1b63f73","last_reissued_at":"2026-07-05T09:35:01.614281Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:35:01.614281Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Voxeland: Probabilistic Instance-Aware Semantic Mapping with Evidence-based Uncertainty Quantification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Javier Gonzalez-Jimenez, Javier Monroy, Jose-Luis Matez-Bandera, Jose-Raul Ruiz-Sarmiento, Pepe Ojeda","submitted_at":"2024-11-13T16:09:04Z","abstract_excerpt":"Robots in human-centered environments require accurate scene understanding to perform high-level tasks effectively. This understanding can be achieved through instance-aware semantic mapping, which involves reconstructing elements at the level of individual instances. Neural networks, the de facto solution for scene understanding, still face limitations such as overconfident incorrect predictions with out-of-distribution objects or generating inaccurate masks.Placing excessive reliance on these predictions makes the reconstruction susceptible to errors, reducing the robustness of the resulting"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.08727","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/2411.08727/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":"2411.08727","created_at":"2026-07-05T09:35:01.614341+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.08727v1","created_at":"2026-07-05T09:35:01.614341+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.08727","created_at":"2026-07-05T09:35:01.614341+00:00"},{"alias_kind":"pith_short_12","alias_value":"7EB5KF5EKHCJ","created_at":"2026-07-05T09:35:01.614341+00:00"},{"alias_kind":"pith_short_16","alias_value":"7EB5KF5EKHCJCRN6","created_at":"2026-07-05T09:35:01.614341+00:00"},{"alias_kind":"pith_short_8","alias_value":"7EB5KF5E","created_at":"2026-07-05T09:35:01.614341+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30293","citing_title":"CSAR: Containerized System Architecture for Robotics","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7EB5KF5EKHCJCRN6EOX4NDBI36","json":"https://pith.science/pith/7EB5KF5EKHCJCRN6EOX4NDBI36.json","graph_json":"https://pith.science/api/pith-number/7EB5KF5EKHCJCRN6EOX4NDBI36/graph.json","events_json":"https://pith.science/api/pith-number/7EB5KF5EKHCJCRN6EOX4NDBI36/events.json","paper":"https://pith.science/paper/7EB5KF5E"},"agent_actions":{"view_html":"https://pith.science/pith/7EB5KF5EKHCJCRN6EOX4NDBI36","download_json":"https://pith.science/pith/7EB5KF5EKHCJCRN6EOX4NDBI36.json","view_paper":"https://pith.science/paper/7EB5KF5E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.08727&json=true","fetch_graph":"https://pith.science/api/pith-number/7EB5KF5EKHCJCRN6EOX4NDBI36/graph.json","fetch_events":"https://pith.science/api/pith-number/7EB5KF5EKHCJCRN6EOX4NDBI36/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7EB5KF5EKHCJCRN6EOX4NDBI36/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7EB5KF5EKHCJCRN6EOX4NDBI36/action/storage_attestation","attest_author":"https://pith.science/pith/7EB5KF5EKHCJCRN6EOX4NDBI36/action/author_attestation","sign_citation":"https://pith.science/pith/7EB5KF5EKHCJCRN6EOX4NDBI36/action/citation_signature","submit_replication":"https://pith.science/pith/7EB5KF5EKHCJCRN6EOX4NDBI36/action/replication_record"}},"created_at":"2026-07-05T09:35:01.614341+00:00","updated_at":"2026-07-05T09:35:01.614341+00:00"}