Flow-based GAN for 3D Point Cloud Generation from a Single Image

Yao Wei, G. Vosselman, M. Yang
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引用次数: 3

Abstract

Generating a 3D point cloud from a single 2D image is of great importance for 3D scene understanding applications. To reconstruct the whole 3D shape of the object shown in the image, the existing deep learning based approaches use either explicit or implicit generative modeling of point clouds, which, however, suffer from limited quality. In this work, we aim to alleviate this issue by introducing a hybrid explicit-implicit generative modeling scheme, which inherits the flow-based explicit generative models for sampling point clouds with arbitrary resolutions while improving the detailed 3D structures of point clouds by leveraging the implicit generative adversarial networks (GANs). We evaluate on the large-scale synthetic dataset ShapeNet, with the experimental results demonstrating the superior performance of the proposed method. In addition, the generalization ability of our method is demonstrated by performing on cross-category synthetic images as well as by testing on real images from PASCAL3D+ dataset.
基于流的GAN从单幅图像生成三维点云
从单幅二维图像生成三维点云对于三维场景理解应用具有重要意义。为了重建图像中显示的物体的整个3D形状,现有的基于深度学习的方法使用显式或隐式的点云生成建模,然而,这些方法的质量有限。在这项工作中,我们的目标是通过引入一种混合显式-隐式生成建模方案来缓解这一问题,该方案继承了基于流的显式生成模型,用于任意分辨率的采样点云,同时通过利用隐式生成对抗网络(gan)改善点云的详细3D结构。在大规模合成数据集ShapeNet上进行了测试,实验结果证明了该方法的优越性能。此外,通过对PASCAL3D+数据集的跨类别合成图像和真实图像进行测试,证明了该方法的泛化能力。
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