Abstract
Deep learning with 3D data such as reconstructed point
clouds and CAD models has received great research interests recently. However, the capability of using point clouds
with convolutional neural network has been so far not fully
explored. In this paper, we present a convolutional neural
network for semantic segmentation and object recognition
with 3D point clouds. At the core of our network is pointwise convolution, a new convolution operator that can be
applied at each point of a point cloud. Our fully convolutional network design, while being surprisingly simple to
implement, can yield competitive accuracy in both semantic
segmentation and object recognition task