  We present a new 3D point-based detector model, named Shift-SSD, for precise
3D object detection in autonomous driving. Traditional point-based 3D object
detectors often employ architectures that rely on a progressive downsampling of
points. While this method effectively reduces computational demands and
increases receptive fields, it will compromise the preservation of crucial
non-local information for accurate 3D object detection, especially in the
complex driving scenarios. To address this, we introduce an intriguing
Cross-Cluster Shifting operation to unleash the representation capacity of the
point-based detector by efficiently modeling longer-range inter-dependency
while including only a negligible overhead. Concretely, the Cross-Cluster
Shifting operation enhances the conventional design by shifting partial
channels from neighboring clusters, which enables richer interaction with
non-local regions and thus enlarges the receptive field of clusters. We conduct
extensive experiments on the KITTI, Waymo, and nuScenes datasets, and the
results demonstrate the state-of-the-art performance of Shift-SSD in both
detection accuracy and runtime efficiency.
