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.

本论文提出了一种有效的点云生成方法，从潜在向量中生成相同形状的多分辨率点云；使用新颖的渐进去卷积网络和基于学习的双边插值，从低分辨率点云开始，通过级联的去卷积层逐渐输出高分辨率的局部和全局特征 maps，使用多层感知器作为生成网络生成多分辨率点云，并提出了形状保持对抗损失来训练点云去卷积生成网络，实验结果表明了方法的有效性。