{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CQWTNLIK7VSU4EBFMJILREAYGS","short_pith_number":"pith:CQWTNLIK","schema_version":"1.0","canonical_sha256":"142d36ad0afd654e10256250b8901834a36661f6cc90b5e54113e1e9f054bbfe","source":{"kind":"arxiv","id":"2508.07483","version":1},"attestation_state":"computed","paper":{"title":"Novel View Synthesis with Gaussian Splatting: Impact on Photogrammetry Model Accuracy and Resolution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Pranav Chougule","submitted_at":"2025-08-10T20:57:36Z","abstract_excerpt":"In this paper, I present a comprehensive study comparing Photogrammetry and Gaussian Splatting techniques for 3D model reconstruction and view synthesis. I created a dataset of images from a real-world scene and constructed 3D models using both methods. To evaluate the performance, I compared the models using structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), learned perceptual image patch similarity (LPIPS), and lp/mm resolution based on the USAF resolution chart. A significant contribution of this work is the development of a modified Gaussian Splatting repository, which "},"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":"2508.07483","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-10T20:57:36Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"3499c26c20cc4a781b3b2eced95fb7956b0fbd379d2b6998b51d304cb3640950","abstract_canon_sha256":"9ca17b8908519749f5471881c3e5e5975394f9aa4eee906e3e893768a9f87034"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:51:46.822395Z","signature_b64":"yuyQ2NLFOKyAPPVSt7a8+ZjI10Kydv3b8wNokBuJO2w41Ut849zKfUhEGty589c2HMDhOfbn2K6pcfPnrxbcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"142d36ad0afd654e10256250b8901834a36661f6cc90b5e54113e1e9f054bbfe","last_reissued_at":"2026-07-05T11:51:46.821853Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:51:46.821853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Novel View Synthesis with Gaussian Splatting: Impact on Photogrammetry Model Accuracy and Resolution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Pranav Chougule","submitted_at":"2025-08-10T20:57:36Z","abstract_excerpt":"In this paper, I present a comprehensive study comparing Photogrammetry and Gaussian Splatting techniques for 3D model reconstruction and view synthesis. I created a dataset of images from a real-world scene and constructed 3D models using both methods. To evaluate the performance, I compared the models using structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), learned perceptual image patch similarity (LPIPS), and lp/mm resolution based on the USAF resolution chart. A significant contribution of this work is the development of a modified Gaussian Splatting repository, which "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.07483","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/2508.07483/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":"2508.07483","created_at":"2026-07-05T11:51:46.821909+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.07483v1","created_at":"2026-07-05T11:51:46.821909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.07483","created_at":"2026-07-05T11:51:46.821909+00:00"},{"alias_kind":"pith_short_12","alias_value":"CQWTNLIK7VSU","created_at":"2026-07-05T11:51:46.821909+00:00"},{"alias_kind":"pith_short_16","alias_value":"CQWTNLIK7VSU4EBF","created_at":"2026-07-05T11:51:46.821909+00:00"},{"alias_kind":"pith_short_8","alias_value":"CQWTNLIK","created_at":"2026-07-05T11:51:46.821909+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2603.18634","citing_title":"SwiftGS: Episodic Priors for Immediate Satellite Surface Recovery","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CQWTNLIK7VSU4EBFMJILREAYGS","json":"https://pith.science/pith/CQWTNLIK7VSU4EBFMJILREAYGS.json","graph_json":"https://pith.science/api/pith-number/CQWTNLIK7VSU4EBFMJILREAYGS/graph.json","events_json":"https://pith.science/api/pith-number/CQWTNLIK7VSU4EBFMJILREAYGS/events.json","paper":"https://pith.science/paper/CQWTNLIK"},"agent_actions":{"view_html":"https://pith.science/pith/CQWTNLIK7VSU4EBFMJILREAYGS","download_json":"https://pith.science/pith/CQWTNLIK7VSU4EBFMJILREAYGS.json","view_paper":"https://pith.science/paper/CQWTNLIK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.07483&json=true","fetch_graph":"https://pith.science/api/pith-number/CQWTNLIK7VSU4EBFMJILREAYGS/graph.json","fetch_events":"https://pith.science/api/pith-number/CQWTNLIK7VSU4EBFMJILREAYGS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CQWTNLIK7VSU4EBFMJILREAYGS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CQWTNLIK7VSU4EBFMJILREAYGS/action/storage_attestation","attest_author":"https://pith.science/pith/CQWTNLIK7VSU4EBFMJILREAYGS/action/author_attestation","sign_citation":"https://pith.science/pith/CQWTNLIK7VSU4EBFMJILREAYGS/action/citation_signature","submit_replication":"https://pith.science/pith/CQWTNLIK7VSU4EBFMJILREAYGS/action/replication_record"}},"created_at":"2026-07-05T11:51:46.821909+00:00","updated_at":"2026-07-05T11:51:46.821909+00:00"}