In additive manufacturing (AM), accurate prediction for the deformation of printed objects contributes to compensation in advance, which is crucial to improving the accuracy of products. Many factors affect the deformation, such as the shape of the object, the properties of the material, and
parameters in the printing process. Existing methods suffer from difficulties in modeling and generalizing between different shapes. In this paper, we formulate the error prediction in AM as a point-wise deviation prediction task and propose a point-based deep neural network to learn the complex deformation patterns by local and global contextual feature
extraction. Furthermore, a data processing flow is proposed for automatically handling the real-scenario data. As an application case, we collect a dataset of dental crowns fabricated by the digital light processing 3D printing and validate the proposed method on the dataset. The results show that our network has a promising ability to predict nonlinear deformation. The proposed method can also be applied to other AM techniques.