Abstract
A comprehensive understanding of 3D scenes is essential for autonomous vehicles (AVs), and among various perception tasks, occupancy estimation plays a central role by providing a general representation of drivable and occupied space. However, most existing occupancy estimation methods rely on LiDAR or cameras, which perform poorly in degraded environments such as smoke, rain, snow, and fog. In this paper, we propose 4D-ROLLS, the first weakly supervised occupancy estimation method for 4D radar using the LiDAR point cloud as the supervisory signal. Specifically, we introduce a method for generating pseudo-LiDAR labels, including occupancy queries and LiDAR height maps, as multi-stage supervision to train the 4D radar occupancy estimation model. Then the model is aligned with the occupancy map produced by LiDAR, fine-tuning its accuracy in occupancy estimation. Extensive comparative experiments validate the exceptional performance of 4D-ROLLS. Its robustness in degraded environments and effectiveness in cross-dataset training are qualitatively demonstrated. The model is also seamlessly transferred to downstream tasks BEV segmentation and point cloud occupancy prediction, highlighting its potential for broader applications. The lightweight network enables 4D-ROLLS model to achieve fast inference speeds at about 30 Hz on a 4060 GPU. The code of 4D-ROLLS will be made available at this https URL.
Abstract (translated)
对三维场景的全面理解对于自动驾驶车辆（AVs）至关重要，在各种感知任务中，占据估计通过提供可行驶和被占用空间的一般表示起着核心作用。然而，大多数现有的占据估计方法依赖于激光雷达或摄像头，在烟雾、雨雪和大雾等退化环境中表现不佳。在本文中，我们提出了4D-ROLLS，这是第一个使用激光雷达点云作为监督信号的弱监督4D雷达占据估计方法。 具体而言，我们引入了一种生成伪激光雷达标签的方法，包括占据查询和激光雷达高度图，作为多阶段监督来训练4D雷达占据估计模型。然后将该模型与由激光雷达产生的占据地图对齐，进一步微调其占据估计的准确性。广泛的对比实验验证了4D-ROLLS的卓越性能，其在退化环境中的鲁棒性和跨数据集训练的有效性得到了定性的证明。 此外，该模型还无缝转移至下游任务如鸟瞰图分割和点云占据预测中，凸显了它在更广泛的应用领域的潜力。轻量级网络使4D-ROLLS模型能够在配备NVIDIA 4060 GPU的设备上实现大约30Hz的快速推理速度。4D-ROLLS代码将在[此链接](https://example.com/4DROLLS)提供。 （注：上述翻译中提到的URL为示例，实际请替换为正确的网址。）
URL
https://arxiv.org/abs/2505.13905
