Figure 1.

Three-Dimensional Generalized Intersection over Union (3D-GIoU) Architecture. The network takes point cloud as input. After the point cloud is discretized into 3D voxel grids, Point-Voxel Feature Encoder is used to learn voxel-wise features. Then, these features are processed by Sparse Convolution Middle Layers and sent to the Region Proposal Network to predict the classification score and the bounding box regression map. Last, the detection results and ground truth bounding boxes are used to calculate 3D GIoU loss, and 3D GIoU loss is used for optimizing the bounding box regression.