  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
