  In this paper, we propose an effective point cloud generation method, which
can generate multi-resolution point clouds of the same shape from a latent
vector. Specifically, we develop a novel progressive deconvolution network with
the learning-based bilateral interpolation. The learning-based bilateral
interpolation is performed in the spatial and feature spaces of point clouds so
that local geometric structure information of point clouds can be exploited.
Starting from the low-resolution point clouds, with the bilateral interpolation
and max-pooling operations, the deconvolution network can progressively output
high-resolution local and global feature maps. By concatenating different
resolutions of local and global feature maps, we employ the multi-layer
perceptron as the generation network to generate multi-resolution point clouds.
In order to keep the shapes of different resolutions of point clouds
consistent, we propose a shape-preserving adversarial loss to train the point
cloud deconvolution generation network. Experimental results demonstrate the
effectiveness of our proposed method.
