. Unsupervised 3D point clouds generated by our tree-GAN for multiple classes (e.g., Motorbike, Laptop, Sofa, Guitar, Skateboard, Knife, Table, Pistol, and Car from top-left to bottom-right). Our tree-GAN can generate more accurate point clouds than baseline (i.e., r-GAN [1]), and can also produce point clouds for semantic parts of objects, which are denoted by different colors. In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN. To achieve state-of-the-art performance for multi-class 3D point cloud generation, a tree-structured graph convolution network (TreeGCN) is introduced as a generator for tree-GAN. Because TreeGCN performs graph convolutions within a tree, it can use ancestor information to boost the representation power for features. To evaluate GANs for 3D point clouds accurately, we develop a novel evaluation metric called Fréchet point cloud distance (FPD). Experimental results demonstrate that the proposed tree-GAN outperforms state-of-the-art GANs in * Authors contributed equally terms of both conventional metrics and FPD, and can generate point clouds for different semantic parts without prior knowledge. The source code is available at https:// github.com/seowok/TreeGAN .
Very recently, Shu et al . REF proposed Tree-GAN with a novel tree-structured graph convolutional neural network as the generator.
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Computer Science