ProbNet: Bayesian deep neural network for point cloud analysis

Abstract

Deep neural networks exhibit extreme superiority in natural language processing and computer vision. With the fast development of 3D sensing technology, point cloud has been wildly used in robotics and autonomous driving. In recent years, several attempts have been made to utilize deep neural networks in the field of point cloud processing. However, most top performing network cannot provide the confidence of each prediction. In this paper, we implement Bayesian deep learning method in point cloud processing and propose ProbConv, a three-dimensional convolutional kernel with stochastic weights. Based on ProbConv, a Bayesian deep neural network named ProbNet is further designed to effectively accomplish classification and segmentation tasks on point cloud data. Since Bayesian neural network can naturally calculate the confidence of its prediction, ProbNet is able to provide the confidence of the segmentation result. The experimental results on ModelNet40, ShapeNet and S3DIS demonstrate that ProbNet improves accuracy in both object classification and semantic segmentation tasks. The confidence provided by the ProbNet is reliable to reflect the accuracy of its prediction.