The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915831423696896 |
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| author | Docea, Reuben Younis, Rayan Long, Yonghao Fleury, Maxime Xu, Jinjing Li, Chenyang Schulze, André Wierick, Ann Bender, Johannes Pfeiffer, Micha Dou, Qi Wagner, Martin Speidel, Stefanie |
| author_facet | Docea, Reuben Younis, Rayan Long, Yonghao Fleury, Maxime Xu, Jinjing Li, Chenyang Schulze, André Wierick, Ann Bender, Johannes Pfeiffer, Micha Dou, Qi Wagner, Martin Speidel, Stefanie |
| contents | The D4D Dataset provides paired endoscopic video and high-quality structured-light geometry for evaluating 3D reconstruction of deforming abdominal soft tissue in realistic surgical conditions. Data were acquired from six porcine cadaver sessions using a da Vinci Xi stereo endoscope and a Zivid structured-light camera, registered via optical tracking and manually curated iterative alignment methods. Three sequence types - whole deformations, incremental deformations, and moved-camera clips - probe algorithm robustness to non-rigid motion, deformation magnitude, and out-of-view updates. Each clip provides rectified stereo images, per-frame instrument masks, stereo depth, start/end structured-light point clouds, curated camera poses and camera intrinsics. In postprocessing, ICP and semi-automatic registration techniques are used to register data, and instrument masks are created. The dataset enables quantitative geometric evaluation in both visible and occluded regions, alongside photometric view-synthesis baselines. Comprising over 300,000 frames and 369 point clouds across 98 curated recordings, this resource can serve as a comprehensive benchmark for developing and evaluating non-rigid SLAM, 4D reconstruction, and depth estimation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02985 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes Docea, Reuben Younis, Rayan Long, Yonghao Fleury, Maxime Xu, Jinjing Li, Chenyang Schulze, André Wierick, Ann Bender, Johannes Pfeiffer, Micha Dou, Qi Wagner, Martin Speidel, Stefanie Computer Vision and Pattern Recognition The D4D Dataset provides paired endoscopic video and high-quality structured-light geometry for evaluating 3D reconstruction of deforming abdominal soft tissue in realistic surgical conditions. Data were acquired from six porcine cadaver sessions using a da Vinci Xi stereo endoscope and a Zivid structured-light camera, registered via optical tracking and manually curated iterative alignment methods. Three sequence types - whole deformations, incremental deformations, and moved-camera clips - probe algorithm robustness to non-rigid motion, deformation magnitude, and out-of-view updates. Each clip provides rectified stereo images, per-frame instrument masks, stereo depth, start/end structured-light point clouds, curated camera poses and camera intrinsics. In postprocessing, ICP and semi-automatic registration techniques are used to register data, and instrument masks are created. The dataset enables quantitative geometric evaluation in both visible and occluded regions, alongside photometric view-synthesis baselines. Comprising over 300,000 frames and 369 point clouds across 98 curated recordings, this resource can serve as a comprehensive benchmark for developing and evaluating non-rigid SLAM, 4D reconstruction, and depth estimation methods. |
| title | The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.02985 |