解决4D雷达点云稀疏噪声问题，提升3D目标检测精度
SD4R: Sparse-to-Dense Learning for 3D Object Detection with 4D Radar
- 通过前景点生成器增强稀疏点云密度
- 在Delft数据集上实现当前最优检测性能
- 适合自动驾驶中雷达感知场景应用
4D雷达测量为3D感知提供了低成本且抗天气干扰的解决方案。然而，雷达点云固有的稀疏性和噪声给精确的3D目标检测带来重大挑战，亟需有效的点云补全方法。尽管已有进展，现有补全方法常无法应对4D雷达点云的极端稀疏性，且在点数极少的场景下鲁棒性不足。本文提出SD4R框架，将稀疏雷达点云转化为密集表示。首先利用前景点生成器（FPG）抑制噪声传播并生成稠密点云；随后，对传统柱状化进行改进的逻辑查询编码器（LQE）构建鲁棒特征表示。实验表明，SD4R在公开的View-of-Delft数据集上表现优异，达到当前最佳水平。代码已开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。