PU-Mask: 3D Point Cloud Upsampling via an Implicit Virtual Mask

We present PU-Mask, a virtual mask-based network for 3D point cloud upsampling. Unlike existing upsampling methods, which treat point cloud upsampling as an “unconstrained generative” problem, we propose to address it from the perspecitive of “local filling”, i.e., we assume that the sparse input point cloud (i.e., the unmasked point set) is obtained by locally masking the original dense point cloud with virtual masks. Therefore, given the unmasked point set and virtual masks, our goal is to fill the point set hidden by the virtual masks. Specifically, because the masks do not actually exist, we first locate and form each virtual mask by a virtual mask generation module. Then, we propose a mask-guided transformer-style asymmetric autoencoder (MTAA) to restore the upsampled features. Moreover, we introduce a second-order unfolding attention mechanism to enhance the interaction between the feature channels of MTAA. Next, we generate a coarse upsampled point cloud using a pooling technique that is specific to the virtual masks. Finally, we design a learnable pseudo Laplacian operator to calibrate the coarse upsampled point cloud and generate a refined upsampled point cloud. Extensive experiments demonstrate that PU-Mask is superior to the state-of-the-art methods. Our code will be made available at: https://github.com/liuhaoyun/PU-Mask