Low-Rank Completion Based Normal Guided Lidar Point Cloud Up-Sampling

Pei An, Di Zhu, You Yang, Jie Ma

Abstract: Commercial inexpensive LiDAR sensor generally suffers low vertical resolutions, whose point cloud is sparse and may not be able to satisfy future metaverse applications. LiDAR point cloud up-sampling is a task to increase the vertical resolution while preserving the structural details. Scene representation is the central pillar of point cloud up-sampling. However, the sparsity of point cloud hinders the extraction of scene representation. In this paper, we find that low-rank representation can describe the primary scene structure approximately, and convert up-sampling as low-rank tensor completion problem. To decrease problem complexity, we leverage range view projection to convert the problem as low-rank depth completion, and present a low-rank normal guided up-sampling approach. It uses normal as guidance to smooth range depth. Extensive experiments show that our method outperforms current methods. In 2× up-sampling task, it achieves as low as 41cm of mean absolute error (MAE), which is 282% and 32% smaller than interpolation and traditional matrix completion methods, respectively. Hence, we believe the proposed method benefits to the field of metaverse.