{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VFN6TKGJRHKHALBB4W4BB3BK3U","short_pith_number":"pith:VFN6TKGJ","schema_version":"1.0","canonical_sha256":"a95be9a8c989d4702c21e5b810ec2add3eb3b66bb32c06c35f5a9ae564c3f5d9","source":{"kind":"arxiv","id":"2312.09147","version":2},"attestation_state":"computed","paper":{"title":"Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ding Liang, Song-Hai Zhang, Yangguang Li, Yan-Pei Cao, Yuan-Chen Guo, Zhipeng Yu, Zi-Xin Zou","submitted_at":"2023-12-14T17:18:34Z","abstract_excerpt":"Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain. Despite their progress, these techniques often face limitations due to slow optimization or rendering processes, leading to extensive training and optimization times. In this paper, we introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes 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":"2312.09147","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-14T17:18:34Z","cross_cats_sorted":[],"title_canon_sha256":"80513001333fb9ae9a0379df268dbca9c48251fc17fd4ca593618144ad502721","abstract_canon_sha256":"68f74f79a06ee51dd308cac23a309451b3213b3982a268217c7358ce9950242a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:25:48.829899Z","signature_b64":"KsoF2YtpN/TBf8R5aQg/Lh13lDnJPayguhJXEx+n7KGlNKF+XagdKNgA4x97yCzZMgEGjDbM3YQIaWjX19KCAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a95be9a8c989d4702c21e5b810ec2add3eb3b66bb32c06c35f5a9ae564c3f5d9","last_reissued_at":"2026-07-05T07:25:48.829395Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:25:48.829395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ding Liang, Song-Hai Zhang, Yangguang Li, Yan-Pei Cao, Yuan-Chen Guo, Zhipeng Yu, Zi-Xin Zou","submitted_at":"2023-12-14T17:18:34Z","abstract_excerpt":"Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain. Despite their progress, these techniques often face limitations due to slow optimization or rendering processes, leading to extensive training and optimization times. In this paper, we introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.09147","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/2312.09147/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":"2312.09147","created_at":"2026-07-05T07:25:48.829446+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.09147v2","created_at":"2026-07-05T07:25:48.829446+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.09147","created_at":"2026-07-05T07:25:48.829446+00:00"},{"alias_kind":"pith_short_12","alias_value":"VFN6TKGJRHKH","created_at":"2026-07-05T07:25:48.829446+00:00"},{"alias_kind":"pith_short_16","alias_value":"VFN6TKGJRHKHALBB","created_at":"2026-07-05T07:25:48.829446+00:00"},{"alias_kind":"pith_short_8","alias_value":"VFN6TKGJ","created_at":"2026-07-05T07:25:48.829446+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.18132","citing_title":"Who Generated This 3D Asset? Learning Source Attribution for Generative 3D Models","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16807","citing_title":"DecoRec: Decomposed 3D Scene Reconstruction from Single-View Images via Object-Level Diffusion","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2403.02151","citing_title":"TripoSR: Fast 3D Object Reconstruction from a Single Image","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2404.07191","citing_title":"InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models","ref_index":65,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VFN6TKGJRHKHALBB4W4BB3BK3U","json":"https://pith.science/pith/VFN6TKGJRHKHALBB4W4BB3BK3U.json","graph_json":"https://pith.science/api/pith-number/VFN6TKGJRHKHALBB4W4BB3BK3U/graph.json","events_json":"https://pith.science/api/pith-number/VFN6TKGJRHKHALBB4W4BB3BK3U/events.json","paper":"https://pith.science/paper/VFN6TKGJ"},"agent_actions":{"view_html":"https://pith.science/pith/VFN6TKGJRHKHALBB4W4BB3BK3U","download_json":"https://pith.science/pith/VFN6TKGJRHKHALBB4W4BB3BK3U.json","view_paper":"https://pith.science/paper/VFN6TKGJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.09147&json=true","fetch_graph":"https://pith.science/api/pith-number/VFN6TKGJRHKHALBB4W4BB3BK3U/graph.json","fetch_events":"https://pith.science/api/pith-number/VFN6TKGJRHKHALBB4W4BB3BK3U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VFN6TKGJRHKHALBB4W4BB3BK3U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VFN6TKGJRHKHALBB4W4BB3BK3U/action/storage_attestation","attest_author":"https://pith.science/pith/VFN6TKGJRHKHALBB4W4BB3BK3U/action/author_attestation","sign_citation":"https://pith.science/pith/VFN6TKGJRHKHALBB4W4BB3BK3U/action/citation_signature","submit_replication":"https://pith.science/pith/VFN6TKGJRHKHALBB4W4BB3BK3U/action/replication_record"}},"created_at":"2026-07-05T07:25:48.829446+00:00","updated_at":"2026-07-05T07:25:48.829446+00:00"}