The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes
Reuben Docea, Rayan Younis, Yonghao Long, Maxime Fleury, Jinjing Xu, Chenyang Li, Andr\'e Schulze, Ann Wierick, Johannes Bender, Micha Pfeiffer, Qi Dou, Martin Wagner, Stefanie Speidel

TL;DR
The Dresden Dataset offers a comprehensive collection of endoscopic videos and structured-light geometry data for evaluating 3D reconstruction of deforming abdominal tissues in realistic surgical scenarios, supporting advanced algorithm development.
Contribution
This paper introduces a large, curated dataset combining stereo endoscopic videos and structured-light data for non-rigid 3D reconstruction in surgical environments, enabling robust algorithm benchmarking.
Findings
Dataset includes over 300,000 frames and 369 point clouds.
Supports evaluation of non-rigid SLAM and 4D reconstruction methods.
Provides baseline photometric view-synthesis results.
Abstract
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…
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Taxonomy
TopicsSurgical Simulation and Training · Anatomy and Medical Technology · Optical measurement and interference techniques
