{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WOSSFCLSWFGEZPWEI7CLMAUONB","short_pith_number":"pith:WOSSFCLS","schema_version":"1.0","canonical_sha256":"b3a5228972b14c4cbec447c4b6028e68450c439f3f742800ba0a385c7d6df2ee","source":{"kind":"arxiv","id":"2409.17624","version":2},"attestation_state":"computed","paper":{"title":"HGS-Planner: Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fei Gao, Jieru Zhao, Ke Wu, Rui Jin, Wenchao Ding, Yi Zhao, Zhiwei Zhang, Zhongxue Gan, Zijun Xu","submitted_at":"2024-09-26T08:19:21Z","abstract_excerpt":"In complex missions such as search and rescue,robots must make intelligent decisions in unknown environments, relying on their ability to perceive and understand their surroundings. High-quality and real-time reconstruction enhances situational awareness and is crucial for intelligent robotics. Traditional methods often struggle with poor scene representation or are too slow for real-time use. Inspired by the efficacy of 3D Gaussian Splatting (3DGS), we propose a hierarchical planning framework for fast and high-fidelity active reconstruction. Our method evaluates completion and quality gain t"},"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":"2409.17624","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-09-26T08:19:21Z","cross_cats_sorted":[],"title_canon_sha256":"869f49729ef982373c6c91527edf54a1e3c138dd67745b78d984dab6696bcd14","abstract_canon_sha256":"f920a3e7645b002bd8eaa157bc84f4c2734e6001844e5b7b4a2b0ce10cd61a3c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:49.013237Z","signature_b64":"aRIz3G77NVcPVzcV+EE1qAvnraf49fpxPYg/ia4IokW2WXR/5qYB7P1SO6ldiLR77xM/XFW8RKntDtXcI3q9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3a5228972b14c4cbec447c4b6028e68450c439f3f742800ba0a385c7d6df2ee","last_reissued_at":"2026-07-05T09:17:49.012790Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:49.012790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HGS-Planner: Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fei Gao, Jieru Zhao, Ke Wu, Rui Jin, Wenchao Ding, Yi Zhao, Zhiwei Zhang, Zhongxue Gan, Zijun Xu","submitted_at":"2024-09-26T08:19:21Z","abstract_excerpt":"In complex missions such as search and rescue,robots must make intelligent decisions in unknown environments, relying on their ability to perceive and understand their surroundings. High-quality and real-time reconstruction enhances situational awareness and is crucial for intelligent robotics. Traditional methods often struggle with poor scene representation or are too slow for real-time use. Inspired by the efficacy of 3D Gaussian Splatting (3DGS), we propose a hierarchical planning framework for fast and high-fidelity active reconstruction. Our method evaluates completion and quality gain t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17624","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/2409.17624/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":"2409.17624","created_at":"2026-07-05T09:17:49.012846+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17624v2","created_at":"2026-07-05T09:17:49.012846+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17624","created_at":"2026-07-05T09:17:49.012846+00:00"},{"alias_kind":"pith_short_12","alias_value":"WOSSFCLSWFGE","created_at":"2026-07-05T09:17:49.012846+00:00"},{"alias_kind":"pith_short_16","alias_value":"WOSSFCLSWFGEZPWE","created_at":"2026-07-05T09:17:49.012846+00:00"},{"alias_kind":"pith_short_8","alias_value":"WOSSFCLS","created_at":"2026-07-05T09:17:49.012846+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01367","citing_title":"ActMVS: Active Scene Reconstruction with Monocular Multi-View Stereo","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WOSSFCLSWFGEZPWEI7CLMAUONB","json":"https://pith.science/pith/WOSSFCLSWFGEZPWEI7CLMAUONB.json","graph_json":"https://pith.science/api/pith-number/WOSSFCLSWFGEZPWEI7CLMAUONB/graph.json","events_json":"https://pith.science/api/pith-number/WOSSFCLSWFGEZPWEI7CLMAUONB/events.json","paper":"https://pith.science/paper/WOSSFCLS"},"agent_actions":{"view_html":"https://pith.science/pith/WOSSFCLSWFGEZPWEI7CLMAUONB","download_json":"https://pith.science/pith/WOSSFCLSWFGEZPWEI7CLMAUONB.json","view_paper":"https://pith.science/paper/WOSSFCLS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17624&json=true","fetch_graph":"https://pith.science/api/pith-number/WOSSFCLSWFGEZPWEI7CLMAUONB/graph.json","fetch_events":"https://pith.science/api/pith-number/WOSSFCLSWFGEZPWEI7CLMAUONB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WOSSFCLSWFGEZPWEI7CLMAUONB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WOSSFCLSWFGEZPWEI7CLMAUONB/action/storage_attestation","attest_author":"https://pith.science/pith/WOSSFCLSWFGEZPWEI7CLMAUONB/action/author_attestation","sign_citation":"https://pith.science/pith/WOSSFCLSWFGEZPWEI7CLMAUONB/action/citation_signature","submit_replication":"https://pith.science/pith/WOSSFCLSWFGEZPWEI7CLMAUONB/action/replication_record"}},"created_at":"2026-07-05T09:17:49.012846+00:00","updated_at":"2026-07-05T09:17:49.012846+00:00"}