DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection

Abstract

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 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 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 reduce latency by 30%-60% on KITTI and Waymo datasets. Specifically, the inference speed of our detector can reach 162 FPS and 30 FPS with negligible performance degradation on KITTI and Waymo datasets, respectively