{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ASTEW2AUMUB5NVZSKLDK5DG5EY","short_pith_number":"pith:ASTEW2AU","schema_version":"1.0","canonical_sha256":"04a64b68146503d6d73252c6ae8cdd263a5e634b20ead5caa1633a10f37acf3a","source":{"kind":"arxiv","id":"2406.01467","version":2},"attestation_state":"computed","paper":{"title":"RaDe-GS: Rasterizing Depth in Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Baowen Zhang, Chuan Fang, Ping Tan, Rakesh Shrestha, Xiaoxiao Long, Yixun Liang","submitted_at":"2024-06-03T15:56:58Z","abstract_excerpt":"Gaussian Splatting (GS) has proven to be highly effective in novel view synthesis, achieving high-quality and real-time rendering. However, its potential for reconstructing detailed 3D shapes has not been fully explored. Existing methods often suffer from limited shape accuracy due to the discrete and unstructured nature of Gaussian splats, which complicates the shape extraction. While recent techniques like 2D GS have attempted to improve shape reconstruction, they often reformulate the Gaussian primitives in ways that reduce both rendering quality and computational efficiency. To address the"},"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":"2406.01467","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2024-06-03T15:56:58Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e62f77c734df5869a74731599e91d8f9438d7c0ff5d885908412a37675a12bd6","abstract_canon_sha256":"914bb1bc8b952524b3f2a335316a6f79c9ac382dbefe06c9881ca1cbec7692ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:35:46.854908Z","signature_b64":"g0aaSYHbWYdQmpDE1fUk3mK5z13DiSitHB19wlfLLaCGt+4gP9QK3nXACovDKLh2MtJdFrJWgHj/8HtpiFNnCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04a64b68146503d6d73252c6ae8cdd263a5e634b20ead5caa1633a10f37acf3a","last_reissued_at":"2026-07-05T08:35:46.854437Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:35:46.854437Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RaDe-GS: Rasterizing Depth in Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Baowen Zhang, Chuan Fang, Ping Tan, Rakesh Shrestha, Xiaoxiao Long, Yixun Liang","submitted_at":"2024-06-03T15:56:58Z","abstract_excerpt":"Gaussian Splatting (GS) has proven to be highly effective in novel view synthesis, achieving high-quality and real-time rendering. However, its potential for reconstructing detailed 3D shapes has not been fully explored. Existing methods often suffer from limited shape accuracy due to the discrete and unstructured nature of Gaussian splats, which complicates the shape extraction. While recent techniques like 2D GS have attempted to improve shape reconstruction, they often reformulate the Gaussian primitives in ways that reduce both rendering quality and computational efficiency. To address the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.01467","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/2406.01467/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":"2406.01467","created_at":"2026-07-05T08:35:46.854495+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.01467v2","created_at":"2026-07-05T08:35:46.854495+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.01467","created_at":"2026-07-05T08:35:46.854495+00:00"},{"alias_kind":"pith_short_12","alias_value":"ASTEW2AUMUB5","created_at":"2026-07-05T08:35:46.854495+00:00"},{"alias_kind":"pith_short_16","alias_value":"ASTEW2AUMUB5NVZS","created_at":"2026-07-05T08:35:46.854495+00:00"},{"alias_kind":"pith_short_8","alias_value":"ASTEW2AU","created_at":"2026-07-05T08:35:46.854495+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":14,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01803","citing_title":"PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation","ref_index":56,"is_internal_anchor":false},{"citing_arxiv_id":"2606.13644","citing_title":"Surflo: Consistent 3D Surface Flow Model with Global State","ref_index":89,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26616","citing_title":"Gaussian-Voxel Duet: A Dual-Scaffolding Hybrid Representation for Fast and Accurate Monocular Surface Reconstruction","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2401.03890","citing_title":"A Survey on 3D Gaussian Splatting","ref_index":286,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18252","citing_title":"GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2509.16702","citing_title":"Animalbooth: multimodal feature enhancement for animal subject personalization","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2511.17207","citing_title":"SING3R-SLAM: Submap-based Indoor Monocular Gaussian SLAM with 3D Reconstruction Priors","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2603.24725","citing_title":"Confidence-Based Mesh Extraction from 3D Gaussians","ref_index":73,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11489","citing_title":"3DGS$^3$: Joint Super Sampling and Frame Interpolation for Real-Time Large-Scale 3DGS Rendering","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08739","citing_title":"ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22129","citing_title":"PAGaS: Pixel-Aligned 1DoF Gaussian Splatting for Depth Refinement","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00498","citing_title":"GOR-IS: 3D Gaussian Object Removal in the Intrinsic Space","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10982","citing_title":"{\\Psi}-Map: Panoptic Surface Integrated Mapping Enables Real2Sim Transfer","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13746","citing_title":"ClipGStream: Clip-Stream Gaussian Splatting for Any Length and Any Motion Multi-View Dynamic Scene Reconstruction","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ASTEW2AUMUB5NVZSKLDK5DG5EY","json":"https://pith.science/pith/ASTEW2AUMUB5NVZSKLDK5DG5EY.json","graph_json":"https://pith.science/api/pith-number/ASTEW2AUMUB5NVZSKLDK5DG5EY/graph.json","events_json":"https://pith.science/api/pith-number/ASTEW2AUMUB5NVZSKLDK5DG5EY/events.json","paper":"https://pith.science/paper/ASTEW2AU"},"agent_actions":{"view_html":"https://pith.science/pith/ASTEW2AUMUB5NVZSKLDK5DG5EY","download_json":"https://pith.science/pith/ASTEW2AUMUB5NVZSKLDK5DG5EY.json","view_paper":"https://pith.science/paper/ASTEW2AU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.01467&json=true","fetch_graph":"https://pith.science/api/pith-number/ASTEW2AUMUB5NVZSKLDK5DG5EY/graph.json","fetch_events":"https://pith.science/api/pith-number/ASTEW2AUMUB5NVZSKLDK5DG5EY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ASTEW2AUMUB5NVZSKLDK5DG5EY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ASTEW2AUMUB5NVZSKLDK5DG5EY/action/storage_attestation","attest_author":"https://pith.science/pith/ASTEW2AUMUB5NVZSKLDK5DG5EY/action/author_attestation","sign_citation":"https://pith.science/pith/ASTEW2AUMUB5NVZSKLDK5DG5EY/action/citation_signature","submit_replication":"https://pith.science/pith/ASTEW2AUMUB5NVZSKLDK5DG5EY/action/replication_record"}},"created_at":"2026-07-05T08:35:46.854495+00:00","updated_at":"2026-07-05T08:35:46.854495+00:00"}