3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data
<|reference_start|>3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data: 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<|reference_end|>
@article{zhao20243dref:,
    title={3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and
  Lidar Data},
    author={Xiting Zhao and S"oren Schwertfeger},
    journal={arXiv preprint arXiv:2403.06538},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06538},
    primaryClass={cs.RO cs.CV eess.IV}
}