Reflective surfaces present a persistent challenge for reliable 3D mapping and perception in robotics and autonomous systems. However, existing reflection datasets and benchmarks remain limited to sparse 2D data. This paper introduces the first large-scale 3D reflection detection dataset containing more than 50,000 aligned samples of multi-return Lidar, RGB images, and 2D/3D semantic labels across diverse indoor environments with various reflections. Textured 3D ground truth meshes enable automatic point cloud labeling to provide precise ground truth annotations. Detailed benchmarks evaluate three Lidar point cloud segmentation methods, as well as current state-of-the-art image segmentation networks for glass and mirror detection. The proposed dataset advances reflection detection by providing a comprehensive testbed with precise global alignment, multi-modal data, and diverse reflective objects and materials. It will drive future research towards reliable reflection detection. The dataset is publicly available at http://3dref.github.io

翻译：反射表面为机器人及自主系统中的可靠三维建图与感知带来了持续挑战。然而，现有反射数据集与基准仍局限于稀疏的二维数据。本文首次提出大规模三维反射检测数据集，包含超过50,000组对齐的多回波激光雷达样本、RGB图像以及跨多种室内环境的二维/三维语义标签，并涵盖各类反射现象。带纹理的三维真值网格可实现点云自动标注，提供精确的真值标注。详细基准评估了三种激光雷达点云分割方法，以及当前最先进的玻璃与镜面检测图像分割网络。本数据集通过提供具备精确全局对齐、多模态数据及多样化反射物体与材料的综合测试平台，推动了反射检测领域的发展，将为未来可靠反射检测研究提供驱动。数据集已公开发布于http://3dref.github.io