As an emerging technology and a relatively affordable device, the 4D imaging
radar has already been confirmed effective in performing 3D object detection in
autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point
clouds hinder further performance improvement, and in-depth studies about its
fusion with other modalities are lacking. On the other hand, most of the
camera-based perception methods transform the extracted image perspective view
features into the bird's-eye view geometrically via "depth-based splatting"
proposed in Lift-Splat-Shoot (LSS), and some researchers exploit other modals
such as LiDARs or ordinary automotive radars for enhancement. Recently, a few
works have applied the "sampling" strategy for image view transformation,
showing that it outperforms "splatting" even without image depth prediction.
However, the potential of "sampling" is not fully unleashed. In this paper, we
investigate the "sampling" view transformation strategy on the camera and 4D
imaging radar fusion-based 3D object detection. In the proposed model, LXL,
predicted image depth distribution maps and radar 3D occupancy grids are
utilized to aid image view transformation, called "radar occupancy-assisted
depth-based sampling". Experiments on VoD and TJ4DRadSet datasets show that the
proposed method outperforms existing 3D object detection methods by a
significant margin without bells and whistles. Ablation studies demonstrate
that our method performs the best among different enhancement settings.

该论文探讨了基于 4D 成像雷达和摄像头的图像视图转换策略并提出一种 “雷达占用格辅助深度采样” 的方法，该方法在 VoD 和 TJ4DRadSet 数据集上的实验结果均表现出较高水平的 3D 物体检测性能。