SD4R: Sparse-to-Dense Learning for 3D Object Detection with 4D Radar
This work addresses the problem of robust 3D perception in adverse weather conditions for autonomous driving systems, representing an incremental improvement over existing densification methods.
The paper tackles the challenge of accurate 3D object detection using sparse and noisy 4D radar point clouds by proposing SD4R, a framework that densifies these point clouds, achieving state-of-the-art performance on the View-of-Delft dataset.
4D radar measurements offer an affordable and weather-robust solution for 3D perception. However, the inherent sparsity and noise of radar point clouds present significant challenges for accurate 3D object detection, underscoring the need for effective and robust point clouds densification. Despite recent progress, existing densification methods often fail to address the extreme sparsity of 4D radar point clouds and exhibit limited robustness when processing scenes with a small number of points. In this paper, we propose SD4R, a novel framework that transforms sparse radar point clouds into dense representations. SD4R begins by utilizing a foreground point generator (FPG) to mitigate noise propagation and produce densified point clouds. Subsequently, a logit-query encoder (LQE) enhances conventional pillarization, resulting in robust feature representations. Through these innovations, our SD4R demonstrates strong capability in both noise reduction and foreground point densification. Extensive experiments conducted on the publicly available View-of-Delft dataset demonstrate that SD4R achieves state-of-the-art performance. Source code is available at https://github.com/lancelot0805/SD4R.