{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PHMAMWVFMZX4W55QY75546DFNC","short_pith_number":"pith:PHMAMWVF","schema_version":"1.0","canonical_sha256":"79d8065aa5666fcb77b0c7fbde7865689fa1163514a83326354624c2ff134f4a","source":{"kind":"arxiv","id":"2409.15715","version":1},"attestation_state":"computed","paper":{"title":"Disentangled Generation and Aggregation for Robust Radiance Fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Huachen Gao, Jianbo Jiao, Kaiqiang Xiong, Luyang Tang, Ronggang Wang, Rui Peng, Shihe Shen, Wangze Xu","submitted_at":"2024-09-24T04:01:26Z","abstract_excerpt":"The utilization of the triplane-based radiance fields has gained attention in recent years due to its ability to effectively disentangle 3D scenes with a high-quality representation and low computation cost. A key requirement of this method is the precise input of camera poses. However, due to the local update property of the triplane, a similar joint estimation as previous joint pose-NeRF optimization works easily results in local minima. To this end, we propose the Disentangled Triplane Generation module to introduce global feature context and smoothness into triplane learning, which mitigat"},"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":"2409.15715","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-24T04:01:26Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"96c2e656d11bc0c46ae523def21da681fbd69d5d42da8ed3a9c34bd446131ad1","abstract_canon_sha256":"84a0cd32940921baf438d187588d2dd4d400fba10a1683f88573b4368bbf182b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:10:58.958200Z","signature_b64":"NxsmmXD8x4LmVN3tPXQcy3kMNKYDRe7XP/9Pz6rXnDzHjDcgxRSpeQVa8T2gcjgqqqSr9CnjFLvMFoxwBAWOBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79d8065aa5666fcb77b0c7fbde7865689fa1163514a83326354624c2ff134f4a","last_reissued_at":"2026-07-05T09:10:58.957771Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:10:58.957771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Disentangled Generation and Aggregation for Robust Radiance Fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Huachen Gao, Jianbo Jiao, Kaiqiang Xiong, Luyang Tang, Ronggang Wang, Rui Peng, Shihe Shen, Wangze Xu","submitted_at":"2024-09-24T04:01:26Z","abstract_excerpt":"The utilization of the triplane-based radiance fields has gained attention in recent years due to its ability to effectively disentangle 3D scenes with a high-quality representation and low computation cost. A key requirement of this method is the precise input of camera poses. However, due to the local update property of the triplane, a similar joint estimation as previous joint pose-NeRF optimization works easily results in local minima. To this end, we propose the Disentangled Triplane Generation module to introduce global feature context and smoothness into triplane learning, which mitigat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.15715","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/2409.15715/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":"2409.15715","created_at":"2026-07-05T09:10:58.957833+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.15715v1","created_at":"2026-07-05T09:10:58.957833+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.15715","created_at":"2026-07-05T09:10:58.957833+00:00"},{"alias_kind":"pith_short_12","alias_value":"PHMAMWVFMZX4","created_at":"2026-07-05T09:10:58.957833+00:00"},{"alias_kind":"pith_short_16","alias_value":"PHMAMWVFMZX4W55Q","created_at":"2026-07-05T09:10:58.957833+00:00"},{"alias_kind":"pith_short_8","alias_value":"PHMAMWVF","created_at":"2026-07-05T09:10:58.957833+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PHMAMWVFMZX4W55QY75546DFNC","json":"https://pith.science/pith/PHMAMWVFMZX4W55QY75546DFNC.json","graph_json":"https://pith.science/api/pith-number/PHMAMWVFMZX4W55QY75546DFNC/graph.json","events_json":"https://pith.science/api/pith-number/PHMAMWVFMZX4W55QY75546DFNC/events.json","paper":"https://pith.science/paper/PHMAMWVF"},"agent_actions":{"view_html":"https://pith.science/pith/PHMAMWVFMZX4W55QY75546DFNC","download_json":"https://pith.science/pith/PHMAMWVFMZX4W55QY75546DFNC.json","view_paper":"https://pith.science/paper/PHMAMWVF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.15715&json=true","fetch_graph":"https://pith.science/api/pith-number/PHMAMWVFMZX4W55QY75546DFNC/graph.json","fetch_events":"https://pith.science/api/pith-number/PHMAMWVFMZX4W55QY75546DFNC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PHMAMWVFMZX4W55QY75546DFNC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PHMAMWVFMZX4W55QY75546DFNC/action/storage_attestation","attest_author":"https://pith.science/pith/PHMAMWVFMZX4W55QY75546DFNC/action/author_attestation","sign_citation":"https://pith.science/pith/PHMAMWVFMZX4W55QY75546DFNC/action/citation_signature","submit_replication":"https://pith.science/pith/PHMAMWVFMZX4W55QY75546DFNC/action/replication_record"}},"created_at":"2026-07-05T09:10:58.957833+00:00","updated_at":"2026-07-05T09:10:58.957833+00:00"}