  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, as a new image
view transformation strategy, "sampling" has been applied in a few image-based
detectors and shown to outperform the widely applied "depth-based splatting"
proposed in Lift-Splat-Shoot (LSS), even without image depth prediction.
However, the potential of "sampling" is not fully unleashed. This paper
investigates the "sampling" view transformation strategy on the camera and 4D
imaging radar fusion-based 3D object detection. LiDAR Excluded Lean (LXL)
model, predicted image depth distribution maps and radar 3D occupancy grids are
generated from image perspective view (PV) features and radar bird's eye view
(BEV) features, respectively. They are sent to the core of LXL, called "radar
occupancy-assisted depth-based sampling", to aid image view transformation. We
demonstrated that more accurate view transformation can be performed by
introducing image depths and radar information to enhance the "sampling"
strategy. Experiments on VoD and TJ4DRadSet datasets show that the proposed
method outperforms the state-of-the-art 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.
