Learning shape abstraction by cropping positive cuboid primitives with negative ones

High-quality 3D model abstraction is needed in many graphics or 3D vision tasks to improve the rendering efficiency, increase transmission speed or reduce space occupation. Traditional simplification algorithms for 3D models rely heavily on the mesh topology and ignore objects’ overall structure during optimization. Learning-based methods are then proposed to form an end-to-end regression system for abstraction. However, existing learning-based methods have difficulty representing shapes with hollow or concave structures. We propose a self-supervised learning-based abstraction method for 3D meshes to solve this problem. Our system predicts the positive and negative primitives, where positive primitives are to match the inside part of the shape, and negative primitives represent the hollow region of the shape. More specifically, the bool difference between positive primitives and the object is fed to a network using Iteration error feedback mechanism to predict the negative primitives, which crop the positive primitives to create hollow or concave structures. In addition, we design a new separation loss to prevent a negative primitive from overlapping the object too much. We evaluate the proposed method on the ShapeNetCore dataset by Chamfer Distance and Intersection over Union. The results show that our positive–negative abstraction schema outperforms the baselines.