Enhancing LiDAR Scene Upsampling with Instance-aware Feature-embedding and Attention Mechanism

Scanning LiDAR is one of the widely used sensors in autonomous vehicles; however, the inherent sparsity of LiDAR point clouds often affects its performance. To address this issue, upsampling methods could be employed to enhance low-resolution LiDAR data. Although there have been methods on upsampling of single-object point clouds recently in computer vision, they tend to generate a considerable amount of artifacts when dealing with real-world LiDAR scenes consisting of multiple objects. In this paper, we propose a solution to tackle this problem by introducing an instance embedding auxiliary task and a context attention module. With our auxiliary learning architecture, the network can learn features that benefit both the primary upsampling task and the auxiliary instance embedding task. This training design enables the point generation process to be carried out separately and significantly reduces artifacts of the upsampling results on the SemanticKITTI dataset, particularly in areas surrounding instances. By leveraging these techniques to improve the model's understanding of the relationship between objects and the background in LiDAR scenes, we achieve an overall 4% to 10% improvement in whole-scene upsampling.