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.

翻译：在本文中,我们提出了一个有效的点云生成方法,它能够从潜向矢量中产生同形状的多分辨率云。具体地说,我们开发了一个以学习为基础的双边内插新颖的渐进分解网络。基于学习的双边内插在点云的空间空间和特征空间中进行,以便利用点云的局部几何结构信息。从低分辨率云开始,通过双边间插和最大集合作业,分解网络可以逐步输出高分辨率的地方和全球地貌图。通过对本地和全球地貌图的不同分辨率进行搭配,我们使用多层宽度作为生成多分辨率点云的生成网络。为了保持点云不同分辨率的形状的一致性,我们提议了一种保持形状的对抗性损失来训练点云分解生成网络。实验结果显示了我们拟议的方法的有效性。