Transcription of VoxelNet: End-to-End Learning for Point Cloud Based 3D ...
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VoxelNet: End-to-End Learning for Point Cloud Based 3D ...
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object DetectionYin ZhouApple TuzelApple detection of objects in 3D Point clouds is acentral problem in many applications, such as autonomousnavigation, housekeeping robots, and augmented/virtual re-ality. To interface a highly sparse LiDAR Point Cloud with aregion proposal network (RPN), most existing efforts havefocused on hand-crafted feature representations, for exam-ple, a bird s eye view projection. In this work, we removethe need of manual feature engineering for 3D Point cloudsand propose VoxelNet, a generic 3D detection network thatunifies feature extraction and bounding box prediction intoa single stage, End-to-End trainable deep network. Specifi-cally, VoxelNet divides a Point Cloud into equally spaced 3 Dvoxels and transforms a group of points within each voxelinto a unified feature representation through the newly in-troduced voxel feature encoding (VFE) layer.
Recently, Qi et al.[31] proposed PointNet, an end-to-end deep neural network that learns point-wise features di-rectly from point clouds. This approach demonstrated im-pressive results on 3D object recognition, 3D object part segmentation, and point-wise semantic segmentation tasks. In [32], an improved version of PointNet was introduced
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