Fast Point Clouds Upsampling with Uncertainty Quantification for Autonomous Vehicles

3D LiDAR is widely used in autonomous systems such as self-driving cars and autonomous robots because it provides accurate 3D point clouds of the surrounding environment under harsh conditions. However, a high-resolution LiDAR is expensive and bulky. Although a low-resolution LiDAR is compact and affordable, the obtained point clouds are so sparse that it is difficult to extract features that are meaningful for highlevel tasks. To solve this problem, several upsampling-based approaches have been proposed by estimating high-resolution point clouds from low-resolution point clouds. However, most works have focused on upsampling object-level or synthetic point clouds obtained from CAD models. Additionally, these approaches have a high computational cost, which makes them unusable in real-time applications such as autonomous driving vehicles. In this paper, we propose a real-time upsampling method with LiDAR for outdoor environments. The proposed method builds on conditional neural processes that are capable of uncertainty quantification. With this probabilistic property, we can remove the upsampled points that have high uncertainty, thus achieving high accuracy. Additionally, the proposed method can be trained in a simulated environment, and then directly applied to the real world. The experimental results on a simulated environment and a real-world dataset show that the proposed method is significantly faster than the state-of-the-art methods while achieving comparable performance.