{"url":"/dataset/tanks-and-temples","name":"Tanks and Temples","full_name":null,"description_markdown":"We present a benchmark for image-based 3D reconstruction. The benchmark sequences were acquired outside the lab, in realistic conditions. Ground-truth data was captured using an industrial laser scanner. The benchmark includes both outdoor scenes and indoor environments. High-resolution video sequences are provided as input, supporting the development of novel pipelines that take advantage of video input to increase reconstruction fidelity. We report the performance of many image-based 3D reconstruction pipelines on the new benchmark. The results point to exciting challenges and opportunities for future work.\r\n\r\nPaper: Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene\r\nreconstruction. In ACM Transactions on Graphics (TOG), 2017.","description_withheld":null,"homepage":"https://www.tanksandtemples.org/","introduced_date":"2017-07-30","introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 3.0","url":"https://www.tanksandtemples.org/license/"},"modalities":[],"tasks":[{"name":"3D Reconstruction","url":"/task/3d-reconstruction","datasets_with_task":"/datasets/task/3d-reconstruction"},{"name":"Novel View Synthesis","url":"/task/novel-view-synthesis","datasets_with_task":"/datasets/task/novel-view-synthesis"},{"name":"Point Clouds","url":"/task/point-clouds","datasets_with_task":"/datasets/task/point-clouds"}],"languages":[],"variants":["Tanks and Temples"],"data_loaders":[],"num_papers_in_archive":55,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/point-clouds-on-tanks-and-temples","task":"Point Clouds","dataset_variant":"Tanks and Temples","rows":21,"metrics":["Mean F1 (Advanced)","Mean F1 (Intermediate)"],"first_row_in_archive_order":{"model":"MVSFormer++","paper":"/paper/mvsformer-revealing-the-devil-in-transformer","metrics":{"Mean F1 (Advanced)":"41.70","Mean F1 (Intermediate)":"67.03"},"code_links":[{"title":"maybelx/mvsformerplusplus","url":"https://github.com/maybelx/mvsformerplusplus"}]},"note":"rows are the archive's own order at snapshot; 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not a correctness claim."}},{"paper":"/paper/compact-3d-scene-representation-via-self","title":"Compact 3D Scene Representation via Self-Organizing Gaussian Grids","date":"2023-12-19","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/compact3d-compressing-gaussian-splat-radiance","title":"CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization","date":"2023-11-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/compact-3d-gaussian-representation-for","title":"Compact 3D Gaussian Representation for Radiance Field","date":"2023-11-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; 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not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}