Classification and Segmentation of 3d Point Clouds based on deep learning

Abstract

Analysis of point clouds through deep convolutional neural networks is an active area of research due to their massive real-world applications including autonomous driving, indoor navigation, robotics, virtual/augmented reality, unmanned aerial vehicles and drones technology. However, to capture fine grained geometric and semantic properties for the underlying recognition task with raw point cloud is exceedingly challenging due to their irregular and unordered nature, sparsity and lack of implicit neighborhood. In this paper, we have introduced a deep, hierarchical, 3d point based architecture to address the highly challenging problem of object classification and part segmentation using raw point cloud. The proposed architecture consists of multiple layers of Sampling, Annular convolution and Pooling, cascaded together in accordance with the principle of deep residual learning. In the skip connections of our deep residual design, we propose to use a combination of linear Projection shortcut and nonlinear Relu group normalization shortcut with batch normalization, to improve both the optimization landscape and representational power. Our network achieves on par or even better than state of the art results on synthetic and real-world benchmark datasets of object classification i.e. MODELNET40 and ScanObjectNN and part segmentation i.e. ShapeNet-part.