SharpNet: A deep learning method for normal vector estimation of point cloud with sharp features

Abstract

The normal vector is a basic attribute of point clouds. Traditional estimation methods are susceptible to noise and outliers. Recently, it reported that estimation robustness can be greatly improved by introducing Deep Neural Network (DNN), but how to accurately obtain the normal vector of sharp features still needs to be further studied. This paper proposes SharpNet, a DNN framework specializing in sharp features of CAD-like models, to transform problems into feature classification by the discretization of normal vector space. In order to eliminate the discretization error, a normal vector refining method is presented, which uses the difference between the initial normal vectors to distinguish neighborhood points of different local surface patches. Finally, the normal vector can be estimated accurately from the refined neighborhood points. Experiments show that our algorithm can estimate the normal vector of sharp features of CAD-like models accurately in challenging situations, and is superior to other DNN-based methods in terms of efficiency.