Progressive Point Cloud Deconvolution Generation Network

• URL: http://arxiv.org/abs/2007.05361v1
• Date: Fri, 10 Jul 2020 13:07:00 GMT
• Title: Progressive Point Cloud Deconvolution Generation Network
• Authors: Le Hui, Rui Xu, Jin Xie, Jianjun Qian, Jian Yang
• Abstract summary: We propose an effective point cloud generation method, which can generate multi-resolution point clouds of the same shape from a latent vector. 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.
• Score: 37.50448637246364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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