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
Abstract
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
BibTeX
@inproceedings{cvpr2024_lidarnetarealsca,
title = {LiDAR-Net: A Real-scanned 3D Point Cloud Dataset for Indoor Scenes},
author = {Yanwen Guo and Yuanqi Li and Dayong Ren and Xiaohong Zhang and Jiawei Li and Liang Pu and Changfeng Ma and Xiaoyu Zhan and Jie Guo and Mingqiang Wei and Yan Zhang and Piaopiao Yu and Shuangyu Yang and Donghao Ji and Huisheng Ye and Hao Sun and Yansong Liu and Yinuo Chen and Jiaqi Zhu and Hongyu Liu},
booktitle = {CVPR 2024},
year = {2024}
}