Point Encoder GAN: A deep learning model for 3D point cloud inpainting

Abstract

In this paper, we propose a Point Encoder GAN for 3D point cloud inpainting. Different from other 3D object inpainting networks, our network can process point cloud data directly without any labeling and assumption. We use a max-pooling layer to solve the unordered of point cloud during the learning procedure. We add two T-Nets (from PointNet) to the encoderdecoder pipeline, which can yield better feature representation of the input point cloud and a more suitable rotation angle of the output point cloud. We then propose a hybrid reconstruction loss function to measure the difference between the two sets of unordered data. Using small sample models on ModelNet40 only, the proposed Point Encoder GAN yields end-to-end inpainting results surprisingly. Experiment results have shown a high success rate. Several technical measures are used to identify the good qualities of our generated models.