公开了用于腹腔手术中软组织变形三维重建的高精度数据集
The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes
- 结合内窥镜视频与结构光扫描，采集真实手术场景下的变形数据
- 包含超30万帧图像和369个点云，覆盖多种运动模式和遮挡情况
- 适合研究非刚性SLAM、4D重建与深度估计的算法开发者使用
D4D数据集提供了配对的内窥镜视频与高质量结构光几何数据，用于评估真实手术条件下腹腔软组织的三维重建。数据来自六次猪尸体实验，采用da Vinci Xi双目内窥镜与Zivid结构光相机采集，并通过光学跟踪和手动迭代对齐方法进行配准。包含三类序列：整体变形、增量变形及移动相机片段，分别测试算法对非刚性运动、形变幅度和视域外更新的鲁棒性。每段视频提供校正后的立体图像、逐帧器械掩码、立体深度图、起止时刻的结构光点云、标注的相机位姿与内参。后期处理中使用ICP与半自动配准技术完成数据注册，生成器械掩码。该数据集支持可见与遮挡区域的定量几何评估，并提供光度视图合成基线。共包含超过30万帧图像和369个点云，覆盖98段经筛选的记录，可作为非刚性SLAM、4D重建与深度估计方法的综合性基准。
原文摘要 · Abstract (English)
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.
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