Pointwise Convolutional Neural Networks

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 point-wise 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.

# 3D Shape Modeling and Analysis # Computer Science # Advanced Neural Network Applications # Domain Adaptation and Few-Shot Learning

Binh‐Son Hua, Minh-Khoi Tran, Sai-Kit Yeung (2018). "Pointwise Convolutional Neural Networks". IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings. https://doi.org/10.1109/cvpr.2018.00109