A fast and efficient 3D reflection symmetry detector based on neural networks

Determining the 3D reflection symmetry planes from 3D models is very difficult and time-consuming. In this paper, we formulate the symmetry detection as a per-point classification problem and present a deep neural network based method to solve it. During the training procedure, we firstly collect a lot of CAD mesh models with reflection symmetry as the training data, and then convert each mesh model into a dense point cloud with points located on the symmetry planes labeled as positive. Based on the PointNet++ architecture, we train a multiscale deep neural network to capture the reflection symmetry property from the point cloud automatically. In addition, a novel weighted cross-entropy loss function is adopted to balance the positive and the negative samples. During the inference procedure, we firstly feed the down-sampled point cloud into the trained neural network. Then, the output per-point classification result is used to calculate an initial symmetry plane equation with RANSAC strategy and the least square method. Finally, iterative closest point algorithm is performed to optimize the fitted symmetry plane. Experimental results on both the synthetic and the real data demonstrate the efficiency, robustness and flexibility of our approach. Our method is pretty fast and generates comparable or better results than the existing methods.