B. Hua, M. Tran, and S. Yeung. (2017)cite arxiv:1712.05245Comment: 10 pages, 6 figures, 10 tables. Paper accepted to CVPR 2018.
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.