{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:A3PFA4Z4EUTZ3QC6FGDJQYXZW2","short_pith_number":"pith:A3PFA4Z4","schema_version":"1.0","canonical_sha256":"06de50733c25279dc05e29869862f9b690651110f492553490e7fca80a2ce078","source":{"kind":"arxiv","id":"2501.01003","version":2},"attestation_state":"computed","paper":{"title":"EasySplat: View-Adaptive Learning makes 3D Gaussian Splatting Easy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ao Gao, Jian Yang, Luosong Guo, Tao Chen, Ying Tai, Zhao Wang, Zhenyu Zhang","submitted_at":"2025-01-02T01:56:58Z","abstract_excerpt":"3D Gaussian Splatting (3DGS) techniques have achieved satisfactory 3D scene representation. Despite their impressive performance, they confront challenges due to the limitation of structure-from-motion (SfM) methods on acquiring accurate scene initialization, or the inefficiency of densification strategy. In this paper, we introduce a novel framework EasySplat to achieve high-quality 3DGS modeling. Instead of using SfM for scene initialization, we employ a novel method to release the power of large-scale pointmap approaches. Specifically, we propose an efficient grouping strategy based on view"},"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":"2501.01003","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-02T01:56:58Z","cross_cats_sorted":[],"title_canon_sha256":"29e8e7c32551d21f73973836d177e2c5fc81ca7a27e2bd1a5f5fa7e22106735e","abstract_canon_sha256":"81556458f2e0c3b3b6ac2b598e8b32ef8fce453ff39b4c7fb35e56584d98fe2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:47.573200Z","signature_b64":"ggap0eRrywD/CnvBjRqSwOc4alFo8hcXCJcvE9uw2jq/1fyC5adVGWHVkaM4g99r7zS77o5BzgxatR36g80wCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06de50733c25279dc05e29869862f9b690651110f492553490e7fca80a2ce078","last_reissued_at":"2026-07-05T10:05:47.572677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:47.572677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EasySplat: View-Adaptive Learning makes 3D Gaussian Splatting Easy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ao Gao, Jian Yang, Luosong Guo, Tao Chen, Ying Tai, Zhao Wang, Zhenyu Zhang","submitted_at":"2025-01-02T01:56:58Z","abstract_excerpt":"3D Gaussian Splatting (3DGS) techniques have achieved satisfactory 3D scene representation. Despite their impressive performance, they confront challenges due to the limitation of structure-from-motion (SfM) methods on acquiring accurate scene initialization, or the inefficiency of densification strategy. In this paper, we introduce a novel framework EasySplat to achieve high-quality 3DGS modeling. Instead of using SfM for scene initialization, we employ a novel method to release the power of large-scale pointmap approaches. Specifically, we propose an efficient grouping strategy based on view"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01003","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/2501.01003/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":"2501.01003","created_at":"2026-07-05T10:05:47.572740+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.01003v2","created_at":"2026-07-05T10:05:47.572740+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01003","created_at":"2026-07-05T10:05:47.572740+00:00"},{"alias_kind":"pith_short_12","alias_value":"A3PFA4Z4EUTZ","created_at":"2026-07-05T10:05:47.572740+00:00"},{"alias_kind":"pith_short_16","alias_value":"A3PFA4Z4EUTZ3QC6","created_at":"2026-07-05T10:05:47.572740+00:00"},{"alias_kind":"pith_short_8","alias_value":"A3PFA4Z4","created_at":"2026-07-05T10:05:47.572740+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/A3PFA4Z4EUTZ3QC6FGDJQYXZW2","json":"https://pith.science/pith/A3PFA4Z4EUTZ3QC6FGDJQYXZW2.json","graph_json":"https://pith.science/api/pith-number/A3PFA4Z4EUTZ3QC6FGDJQYXZW2/graph.json","events_json":"https://pith.science/api/pith-number/A3PFA4Z4EUTZ3QC6FGDJQYXZW2/events.json","paper":"https://pith.science/paper/A3PFA4Z4"},"agent_actions":{"view_html":"https://pith.science/pith/A3PFA4Z4EUTZ3QC6FGDJQYXZW2","download_json":"https://pith.science/pith/A3PFA4Z4EUTZ3QC6FGDJQYXZW2.json","view_paper":"https://pith.science/paper/A3PFA4Z4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.01003&json=true","fetch_graph":"https://pith.science/api/pith-number/A3PFA4Z4EUTZ3QC6FGDJQYXZW2/graph.json","fetch_events":"https://pith.science/api/pith-number/A3PFA4Z4EUTZ3QC6FGDJQYXZW2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A3PFA4Z4EUTZ3QC6FGDJQYXZW2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A3PFA4Z4EUTZ3QC6FGDJQYXZW2/action/storage_attestation","attest_author":"https://pith.science/pith/A3PFA4Z4EUTZ3QC6FGDJQYXZW2/action/author_attestation","sign_citation":"https://pith.science/pith/A3PFA4Z4EUTZ3QC6FGDJQYXZW2/action/citation_signature","submit_replication":"https://pith.science/pith/A3PFA4Z4EUTZ3QC6FGDJQYXZW2/action/replication_record"}},"created_at":"2026-07-05T10:05:47.572740+00:00","updated_at":"2026-07-05T10:05:47.572740+00:00"}