  Many point-based 3D detectors adopt point-feature sampling strategies to drop
some points for efficient inference. These strategies are typically based on
fixed and handcrafted rules, making it difficult to handle complicated scenes.
Different from them, we propose a Dynamic Ball Query (DBQ) network to
adaptively select a subset of input points according to the input features, and
assign the feature transform with a suitable receptive field for each selected
point. It can be embedded into some state-of-the-art 3D detectors and trained
in an end-to-end manner, which significantly reduces the computational cost.
Extensive experiments demonstrate that our method can increase the inference
speed by 30%-100% on KITTI, Waymo, and ONCE datasets. Specifically, the
inference speed of our detector can reach 162 FPS on KITTI scene, and 30 FPS on
Waymo and ONCE scenes without performance degradation. Due to skipping the
redundant points, some evaluation metrics show significant improvements. Codes
will be released at https://github.com/yancie-yjr/DBQ-SSD.
