构建了用于喷射混凝土场景的多模态深度感知数据集
ShotcreteDepth: A Bi-modal Dataset for Robust Robotic Depth Perception in Shotcrete Construction Environments
- 采集真实工地中高浊度、弱光照下的双目图像与激光点云
- 包含1.1万组同步数据，220组带标注用于评估
- 适合研究工业环境下鲁棒深度估计与感知系统
我们提出ShotcreteDepth，一个来自建筑领域的双模态数据集，涵盖喷射混凝土作业过程与一般施工环境。数据集包含在高浊度、弱光照等恶劣真实条件下获取的立体RGB图像与激光雷达点云，此类条件导致传感器测量不完整且噪声大，严重挑战自主系统中的感知能力。同时发布一款轻量级标注工具，可高效标注激光点云。数据集共含11,252组时间同步样本，其中220组已标注用于评估。该数据集支持立体匹配、深度补全及复杂工业场景下的深度估计研究。项目仓库：https://github.com/dtu-pas/shotcrete-depth
原文摘要 · Abstract (English)
We introduce ShotcreteDepth, a bi-modal dataset from the construction domain that captures both an active shotcreting process and general construction environments. The dataset comprises stereo RGB imagery and LiDAR point clouds acquired under harsh real-world conditions, including high turbidity and poor illumination. Such conditions adversely affect sensor measurements, leading to incomplete and noisy observations that pose significant challenges for perception systems in autonomous applications. Alongside the dataset, we release a lightweight annotation tool designed for time-efficient labeling of LiDAR point clouds. ShotcreteDepth consists of 11,252 temporally synchronized data samples, of which 220 are annotated for evaluation purposes. The dataset supports research in stereo matching, depth completion, and depth estimation under conditions that closely reflect the operational complexities found in industrial settings. Project repository: https://github.com/dtu-pas/shotcrete-depth
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