{"url":"/sota/novel-view-synthesis-on-mip-nerf-360","task":{"name":"Novel View Synthesis","url":"/task/novel-view-synthesis","note":null},"dataset":{"name":"Mip-NeRF 360","url":"/dataset/mip-nerf-360"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"Synthesize a target image with an arbitrary target camera pose from given source images and their camera poses.\r\n\r\nSee [Wiki](https://en.wikipedia.org/wiki/View_synthesis) for more introductions.\r\n\r\nThe Synthesis method include: NeRF, MPI and so on.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Multi-view to Novel view: Synthesizing novel views with Self-Learned Confidence](https://github.com/shaohua0116/Multiview2Novelview) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["LPIPS","PSNR","SSIM","Size (MB)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"LPIPS":null,"PSNR":"higher","SSIM":"higher","Size (MB)":null}},"counts":{"rows":14,"rows_with_code":12,"rows_with_paper_page":12,"rows_dated":12,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MVGS","metrics":{"LPIPS":"0.171","PSNR":"29.82","SSIM":"0.877"},"uses_additional_data":false,"paper_date":"2024-10-02","paper":"/paper/mvgs-multi-view-regulated-gaussian-splatting","paper_url":"https://arxiv.org/abs/2410.02103v1","paper_title":"MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis","code":"https://github.com/xiaobiaodu/MVGS","n_code_links":1,"syntology":{"n_ran":6,"n_unverified":2,"n_samples":8,"n_pointer_only_licence":8}},{"rank_in_archive_order":2,"model":"RadSplat","metrics":{"LPIPS":"0.171","PSNR":"28.14","SSIM":"0.843"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"LightGaussian","metrics":{"LPIPS":"0.21","PSNR":"28.45","SSIM":"0.857"},"uses_additional_data":false,"paper_date":"2023-11-28","paper":"/paper/lightgaussian-unbounded-3d-gaussian","paper_url":"https://arxiv.org/abs/2311.17245v6","paper_title":"LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS","code":"https://github.com/VITA-Group/LightGaussian","n_code_links":1,"syntology":{"n_ran":17,"n_unverified":5,"n_samples":22,"n_pointer_only_licence":22}},{"rank_in_archive_order":4,"model":"3DGEER","metrics":{"LPIPS":"0.210","PSNR":"27.76","SSIM":"0.821"},"uses_additional_data":false,"paper_date":"2025-05-29","paper":"/paper/3dgeer-exact-and-efficient-volumetric","paper_url":"https://arxiv.org/abs/2505.24053v1","paper_title":"3DGEER: Exact and Efficient Volumetric Rendering with 3D Gaussians","code":"https://github.com/zixunh/3DGEER","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"3D Gaussian Splatting","metrics":{"LPIPS":"0.214","PSNR":"27.21","SSIM":"0.815"},"uses_additional_data":false,"paper_date":"2023-08-08","paper":"/paper/3d-gaussian-splatting-for-real-time-radiance","paper_url":"https://arxiv.org/abs/2308.04079v1","paper_title":"3D Gaussian Splatting for Real-Time Radiance Field Rendering","code":"https://github.com/graphdeco-inria/gaussian-splatting","n_code_links":2,"syntology":{"n_ran":6,"n_unverified":4,"n_samples":10,"n_pointer_only_licence":10}},{"rank_in_archive_order":6,"model":"Self-Organizing Gaussians","metrics":{"LPIPS":"0.22","PSNR":"27.64","SSIM":"0.864","Size (MB)":"40.3"},"uses_additional_data":false,"paper_date":"2023-12-19","paper":"/paper/compact-3d-scene-representation-via-self","paper_url":"https://arxiv.org/abs/2312.13299v2","paper_title":"Compact 3D Scene Representation via Self-Organizing Gaussian Grids","code":"https://github.com/facebookresearch/uco3d","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":7,"model":"Compact3D","metrics":{"LPIPS":"0.228","PSNR":"27.16","SSIM":"0.808"},"uses_additional_data":false,"paper_date":"2023-11-30","paper":"/paper/compact3d-compressing-gaussian-splat-radiance","paper_url":"https://arxiv.org/abs/2311.18159v3","paper_title":"CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization","code":"https://github.com/ucdvision/compact3d","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":8,"model":"HAC 3DGS","metrics":{"LPIPS":"0.230","PSNR":"27.77","SSIM":"0.811"},"uses_additional_data":false,"paper_date":"2024-03-21","paper":"/paper/hac-hash-grid-assisted-context-for-3d","paper_url":"https://arxiv.org/abs/2403.14530v3","paper_title":"HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression","code":"https://github.com/yihangchen-ee/hac","n_code_links":2,"syntology":null},{"rank_in_archive_order":9,"model":"Compressed 3D Gaussian Splatting","metrics":{"LPIPS":"0.238","PSNR":"26.98","SSIM":"0.80"},"uses_additional_data":false,"paper_date":"2023-11-17","paper":"/paper/compressed-3d-gaussian-splatting-for","paper_url":"https://arxiv.org/abs/2401.02436v2","paper_title":"Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis","code":"https://github.com/KeKsBoTer/c3dgs","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":3,"n_samples":10,"n_pointer_only_licence":10}},{"rank_in_archive_order":10,"model":"C3DGS","metrics":{"LPIPS":"0.247","PSNR":"27.08","SSIM":"0.798"},"uses_additional_data":false,"paper_date":"2023-11-22","paper":"/paper/compact-3d-gaussian-representation-for","paper_url":"https://arxiv.org/abs/2311.13681v2","paper_title":"Compact 3D Gaussian Representation for Radiance Field","code":"https://github.com/maincold2/Compact-3DGS","n_code_links":1,"syntology":{"n_ran":5,"n_unverified":2,"n_samples":7,"n_pointer_only_licence":7}},{"rank_in_archive_order":11,"model":"NeRF++","metrics":{"LPIPS":"0.427","PSNR":"22.76","SSIM":"0.548"},"uses_additional_data":false,"paper_date":"2022-07-30","paper":"/paper/mobilenerf-exploiting-the-polygon","paper_url":"https://arxiv.org/abs/2208.00277v5","paper_title":"MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures","code":"https://github.com/google-research/jax3d","n_code_links":1,"syntology":{"n_ran":9,"n_unverified":0,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":12,"model":"MobileNeRF","metrics":{"LPIPS":"0.47","PSNR":"21.95","SSIM":"0.47"},"uses_additional_data":false,"paper_date":"2022-07-30","paper":"/paper/mobilenerf-exploiting-the-polygon","paper_url":"https://arxiv.org/abs/2208.00277v5","paper_title":"MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures","code":"https://github.com/google-research/jax3d","n_code_links":1,"syntology":{"n_ran":9,"n_unverified":0,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":13,"model":"NeRF","metrics":{"LPIPS":"0.515","PSNR":"21.46","SSIM":"0.458"},"uses_additional_data":false,"paper_date":"2022-07-30","paper":"/paper/mobilenerf-exploiting-the-polygon","paper_url":"https://arxiv.org/abs/2208.00277v5","paper_title":"MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures","code":"https://github.com/google-research/jax3d","n_code_links":1,"syntology":{"n_ran":9,"n_unverified":0,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":14,"model":"Li Auto Inc.","metrics":{"LPIPS":"NaN","PSNR":"Inf","SSIM":"Inf"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,885 of the 9,623 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9623,"papers_checked":6885,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":2737},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":["LPIPS","PSNR","SSIM"],"entries":[{"paper":"/paper/arxiv-2607-00595","paper_title":"GADA: Geometry-Aware Deformable Aggregation for Image-Based Gaussian Splatting","arxiv_id":"2607.00595","model":"Ours","row_label":"Ours","configuration":null,"configuration_in_model":false,"configuration_not_shown_because":null,"values":{"LPIPS":"0.179","PSNR":"28.62","SSIM":"0.840"},"date":"2026-07-01","month_from_arxiv_id":null,"where_in_paper":"Table 1, row “Ours”","table_label":"1","table_label_from":"the caption's own label","table_position_1based":1,"caption_head":"Table 1 : Quantitative comparison with […] methods on three…","cells":[{"column":"LPIPS","cell_text":"0.179","column_header":"Mip-NeRF 360 / LPIPS \\downarrow","value":0.179,"table_index_0based":0,"row":12,"col":3},{"column":"PSNR","cell_text":"28.62","column_header":"Mip-NeRF 360 / PSNR \\uparrow","value":28.62,"table_index_0based":0,"row":12,"col":1},{"column":"SSIM","cell_text":"0.840","column_header":"Mip-NeRF 360 / SSIM \\uparrow","value":0.84,"table_index_0based":0,"row":12,"col":2}],"code":"repository linked, no samples harvested"}]},"syntology":{"read_at":"2026-09-25T09:33:49+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":10,"rows_with_any_sample_ran":10,"distinct_papers_with_graph_line":8,"distinct_papers_with_any_sample_ran":8,"samples_over_distinct_papers":{"n_ran":52,"n_unverified":17,"n_samples":69,"n_pointer_only_licence":58,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":70,"n_unverified":17,"n_samples":87,"n_pointer_only_licence":58,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}