Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RPN), most existing efforts have focused on hand-crafted feature representations, for example, a bird's eye view projection. In this work, we remove the need of manual feature engineering for 3D point clouds and propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network. Specifically, VoxelNet divides a point cloud into equally spaced 3D voxels and transforms a group of points within each voxel into a unified feature representation through the newly introduced voxel feature encoding (VFE) layer. In this way, the point cloud is encoded as a descriptive volumetric representation, which is then connected to a RPN to generate detections. Experiments on the KITTI car detection benchmark show that VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a large margin. Furthermore, our network learns an effective discriminative representation of objects with various geometries, leading to encouraging results in 3D detection of pedestrians and cyclists, based on only LiDAR. Recently, Qi et al. [31] proposed PointNet, an end-toend deep neural network that learns point-wise features directly from point clouds. This approach demonstrated impressive results on 3D object recognition, 3D object part segmentation, and point-wise semantic segmentation tasks. In [32] , an improved version of PointNet was introduced which enabled the network to learn local structures at different scales. To achieve satisfactory results, these two approaches trained feature transformer networks on all input points (∼1k points). Since typical point clouds obtained using LiDARs contain ∼100k points, training the architec-4490
VoxelNet REF directly generates detections from a Region Proposal Network using a voxelized point cloud.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science