Progressive Point Cloud Deconvolution Generation Network
arXiv:2007.05361
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.
Accepted to ECCV 2020; Project page: https://github.com/fpthink/PDGN
References in corpus (10)
- Adam: A Method for Stochastic Optimization
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Conditional Generative Adversarial Nets
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Empirical Evaluation of Rectified Activations in Convolutional Network
- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
- Learning Representations and Generative Models for 3D Point Clouds
- Dynamic Graph CNN for Learning on Point Clouds
- RGCNN: Regularized Graph CNN for Point Cloud Segmentation
- PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention