DUAL ADVERSARIAL MODEL FOR GENERATING 3D POINT CLOUD
Three-dimensional data, such as point clouds, are often composed of three coordinates with few featrues. In view of this, it is hard for common neural networks to learn and represent the characteristics directly. In this paper, we focus on latent space’s representation of data characteristics, introduce a novel generative framework based on AutoEncoder(AE) and Generative Adversarial Network(GAN) with extra well-designed loss. We embed this framework directly into the raw 3D-GAN, and experiments demonstrate the potential of the framework in regard of improving the performance on the public dataset compared with other point cloud generation models proposed in recent years. It even achieves state of-the-art performance. We also perform experiments on MNIST and exhibit an excellent result on 2D dataset.
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