{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:55GQ6SQ6VPHNRCES47JSAJNFWJ","short_pith_number":"pith:55GQ6SQ6","canonical_record":{"source":{"id":"2506.21629","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-06-24T21:10:06Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9a3a5a6d42bb06455e8793f667f236f1f5ec220e3a62f42082b5d71c81b65bfb","abstract_canon_sha256":"b1c1e93b8589fc8b2cbe4da8f40a7d935d3c4e51c0df8cacfb3de5a68f4814b5"},"schema_version":"1.0"},"canonical_sha256":"ef4d0f4a1eabced88892e7d32025a5b27d303697f8474b820666047840ed0cb4","source":{"kind":"arxiv","id":"2506.21629","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.21629","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"arxiv_version","alias_value":"2506.21629v1","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21629","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"pith_short_12","alias_value":"55GQ6SQ6VPHN","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"pith_short_16","alias_value":"55GQ6SQ6VPHNRCES","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"pith_short_8","alias_value":"55GQ6SQ6","created_at":"2026-07-05T11:29:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:55GQ6SQ6VPHNRCES47JSAJNFWJ","target":"record","payload":{"canonical_record":{"source":{"id":"2506.21629","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-06-24T21:10:06Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9a3a5a6d42bb06455e8793f667f236f1f5ec220e3a62f42082b5d71c81b65bfb","abstract_canon_sha256":"b1c1e93b8589fc8b2cbe4da8f40a7d935d3c4e51c0df8cacfb3de5a68f4814b5"},"schema_version":"1.0"},"canonical_sha256":"ef4d0f4a1eabced88892e7d32025a5b27d303697f8474b820666047840ed0cb4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:14.537577Z","signature_b64":"3leNvmzl4NzGu5f5/iodyqTRLpYg/DHeUFvFB9gW2wpP8pjYmWfoiyJue5IKCJS8vDthNgK6YmnOCZDn2MEPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef4d0f4a1eabced88892e7d32025a5b27d303697f8474b820666047840ed0cb4","last_reissued_at":"2026-07-05T11:29:14.537027Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:14.537027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.21629","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:29:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z9tX8l7k1SdvWwCA/9u3iL0QDfLsb/p7viOPZQ54q1ZIE0OxrzduwOd3vIbDZvepka/wFuZSYIP8eVX5PZ14DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:28:10.134484Z"},"content_sha256":"3e507436705a0ede2f8abd19ccf902603114e5fb329fe40679ec4a75e243d5fa","schema_version":"1.0","event_id":"sha256:3e507436705a0ede2f8abd19ccf902603114e5fb329fe40679ec4a75e243d5fa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:55GQ6SQ6VPHNRCES47JSAJNFWJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ICP-3DGS: SfM-free 3D Gaussian Splatting for Large-scale Unbounded Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Chenhao Zhang, Fengqing Zhu, Yezhi Shen","submitted_at":"2025-06-24T21:10:06Z","abstract_excerpt":"In recent years, neural rendering methods such as NeRFs and 3D Gaussian Splatting (3DGS) have made significant progress in scene reconstruction and novel view synthesis. However, they heavily rely on preprocessed camera poses and 3D structural priors from structure-from-motion (SfM), which are challenging to obtain in outdoor scenarios. To address this challenge, we propose to incorporate Iterative Closest Point (ICP) with optimization-based refinement to achieve accurate camera pose estimation under large camera movements. Additionally, we introduce a voxel-based scene densification approach "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21629","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/2506.21629/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:29:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v25J78kAi6wYNj2SOeMSWsEDtQJPJF/r4Xz1uNqMivmVPYchRmT863bHjxzcNXh4OKPSgGWFRTR9p8ElNu1zBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:28:10.135038Z"},"content_sha256":"99e5928153bda15450c7391c96439711811246ec35657116efc32afa8ce66d58","schema_version":"1.0","event_id":"sha256:99e5928153bda15450c7391c96439711811246ec35657116efc32afa8ce66d58"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/55GQ6SQ6VPHNRCES47JSAJNFWJ/bundle.json","state_url":"https://pith.science/pith/55GQ6SQ6VPHNRCES47JSAJNFWJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/55GQ6SQ6VPHNRCES47JSAJNFWJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T02:28:10Z","links":{"resolver":"https://pith.science/pith/55GQ6SQ6VPHNRCES47JSAJNFWJ","bundle":"https://pith.science/pith/55GQ6SQ6VPHNRCES47JSAJNFWJ/bundle.json","state":"https://pith.science/pith/55GQ6SQ6VPHNRCES47JSAJNFWJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/55GQ6SQ6VPHNRCES47JSAJNFWJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:55GQ6SQ6VPHNRCES47JSAJNFWJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b1c1e93b8589fc8b2cbe4da8f40a7d935d3c4e51c0df8cacfb3de5a68f4814b5","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-06-24T21:10:06Z","title_canon_sha256":"9a3a5a6d42bb06455e8793f667f236f1f5ec220e3a62f42082b5d71c81b65bfb"},"schema_version":"1.0","source":{"id":"2506.21629","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.21629","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"arxiv_version","alias_value":"2506.21629v1","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21629","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"pith_short_12","alias_value":"55GQ6SQ6VPHN","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"pith_short_16","alias_value":"55GQ6SQ6VPHNRCES","created_at":"2026-07-05T11:29:14Z"},{"alias_kind":"pith_short_8","alias_value":"55GQ6SQ6","created_at":"2026-07-05T11:29:14Z"}],"graph_snapshots":[{"event_id":"sha256:99e5928153bda15450c7391c96439711811246ec35657116efc32afa8ce66d58","target":"graph","created_at":"2026-07-05T11:29:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.21629/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, neural rendering methods such as NeRFs and 3D Gaussian Splatting (3DGS) have made significant progress in scene reconstruction and novel view synthesis. However, they heavily rely on preprocessed camera poses and 3D structural priors from structure-from-motion (SfM), which are challenging to obtain in outdoor scenarios. To address this challenge, we propose to incorporate Iterative Closest Point (ICP) with optimization-based refinement to achieve accurate camera pose estimation under large camera movements. Additionally, we introduce a voxel-based scene densification approach ","authors_text":"Chenhao Zhang, Fengqing Zhu, Yezhi Shen","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-06-24T21:10:06Z","title":"ICP-3DGS: SfM-free 3D Gaussian Splatting for Large-scale Unbounded Scenes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21629","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3e507436705a0ede2f8abd19ccf902603114e5fb329fe40679ec4a75e243d5fa","target":"record","created_at":"2026-07-05T11:29:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b1c1e93b8589fc8b2cbe4da8f40a7d935d3c4e51c0df8cacfb3de5a68f4814b5","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-06-24T21:10:06Z","title_canon_sha256":"9a3a5a6d42bb06455e8793f667f236f1f5ec220e3a62f42082b5d71c81b65bfb"},"schema_version":"1.0","source":{"id":"2506.21629","kind":"arxiv","version":1}},"canonical_sha256":"ef4d0f4a1eabced88892e7d32025a5b27d303697f8474b820666047840ed0cb4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ef4d0f4a1eabced88892e7d32025a5b27d303697f8474b820666047840ed0cb4","first_computed_at":"2026-07-05T11:29:14.537027Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:29:14.537027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3leNvmzl4NzGu5f5/iodyqTRLpYg/DHeUFvFB9gW2wpP8pjYmWfoiyJue5IKCJS8vDthNgK6YmnOCZDn2MEPCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:29:14.537577Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.21629","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e507436705a0ede2f8abd19ccf902603114e5fb329fe40679ec4a75e243d5fa","sha256:99e5928153bda15450c7391c96439711811246ec35657116efc32afa8ce66d58"],"state_sha256":"34703ace759590b0fc6821f7dd91a0c853fdea212c30b4b8a54764da27d446f9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BqYRyEvafwpiaSGlMCVfs/+oONEZddl17nilEzW8RRcBzVRWk8oJrM1uB1bTz0tijLuK9k/qn/rPCsw0mQUUDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T02:28:10.140054Z","bundle_sha256":"b9d200b25bd886f9dd43c6ff931a0b1a49ba01c3076cca3745f62b3db86e60d8"}}