LiDAR-Net: A Real-scanned 3D Point Cloud Dataset for Indoor Scenes

Yanwen Guo, Yuanqi Li, Dayong Ren, Xiaohong Zhang, Jiawei Li, Liang Pu, Changfeng Ma, Xiaoyu Zhan, Jie Guo, Mingqiang Wei, Yan Zhang, Piaopiao Yu, Shuangyu Yang, Donghao Ji, Huisheng Ye, Hao Sun, Yansong Liu, Yinuo Chen, Jiaqi Zhu, Hongyu Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 21989-21999

In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset containing nearly 3.6 billion precisely point-level annotated points covering an expansive area of 30000m^2. It encompasses three prevalent daily environments including learning scenes working scenes and living scenes. LiDAR-Net is characterized by its non-uniform point distribution e.g. scanning holes and scanning lines. Additionally it meticulously records and annotates scanning anomalies including reflection noise and ghost. These anomalies stem from specular reflections on glass or metal as well as distortions due to moving persons. LiDAR-Net's realistic representation of non-uniform distribution and anomalies significantly enhances the training of deep learning models leading to improved generalization in practical applications. We thoroughly evaluate the performance of state-of-the-art algorithms on LiDAR-Net and provide a detailed analysis of the results. Crucially our research identifies several fundamental challenges in understanding indoor point clouds contributing essential insights to future explorations in this field. Our dataset can be found online: http://lidar-net.njumeta.com