CBi-GNN: Cross-Scale Bilateral Graph Neural Network for 3D Object Detection

3D object detection from LiDAR point clouds is a challenging task, since the point clouds are irregular and sparse. Existing one-stage methods mainly predict the 3D bounding box of 3D objects by extracting deep down-scaled features of point clouds from low-level (high-resolution, HR) feature maps to high-level (low-resolution, LR). Nonetheless, most of these methods ignore geometric context information of the down-scaled feature maps across scales, especially only using the LR feature will result in incomplete structure and less location accuracy of 3D objects. In this paper, we propose a novel cross-scale graph network-based one-stage 3D object detector to fully exploit the geometric contexts of the voxels between the down-scaled feature maps. Specifically, we first employ a 3D sparse convolution neural network to form different resolutions of feature maps of voxels. We then dynamically construct a cross-scale bilateral graph to search the neighbor non-empty voxels in the HR feature map with a fixed radius for each non-empty voxel in the LR feature map. In the constructed graph, we present a bilateral attention mechanism (i.e., self-attention and spatial attention) in the HR feature map and encode each non-empty voxel in the LR feature map by aggregating the HR features to obtain the attention features. In addition, we design a non-local part pooling operation to improve the score of the detected bounding box of 3D objects. Finally, we formulate a multi-task loss to train our network for regression of the 3D bounding box of the 3D objects. Experiments on the challenging KITTI’s 3D/BEV benchmark show that our proposed detector outperforms all one-stage 3D object detectors and is comparable to two-stage 3D object detectors. Our code is available at https://github.com/csjxchen/CBi-GNN.