Uncertainty-Aware Interactive LiDAR Sampling for Deep Depth Completion

Programmable scan LiDAR is able to measure arbitrary areas and is expected to be used in various applications. In this paper, we study a LiDAR sampling strategy for deep depth completion of a programmable scan LiDAR with an RGB camera. General data sampling strategies include adaptive approaches such as active learning, in which candidate data are assessed through a task model for data selection and then the selected data pool is updated sequentially. Although it is an effective approach, the adaptive approach requires many iterations involving the inference process to assess the candidate data, which is not suitable for LiDAR systems. Therefore, we propose a novel interactive LiDAR sampling method without each inference process. Our key insights are that we assess sampling candidates by depth estimation uncertainty and virtually update the uncertainty by an approximation of the candidate assessment. This enables us to add interactivity to the model state without requiring each inference process. We demonstrate the effectiveness of our method on the KITTI dataset and the generalization performance on the NYU-Depth-v2 dataset in comparison with a conventional adaptive LiDAR sampling method, and we find superior results in the depth completion task. We also show ablation studies to analyze our approach.