Pointivae: Invertible Variational Autoencoder Framework for 3D Point Cloud Generation

Point cloud generation is a challenging task and has drawn great attention in 3D vision community. However, existing methods rarely consider to exploit local features, leading to unsatisfactory generated results that lack of high frequency. In this paper, we put forward a novel point cloud generation framework called PointIVAE, which adopts VAE based framework to construct local relations and enhance generating capability. PointIVAE contains three components, including an encoder, a flow model and a decoder. Specially, the encoder aims to aggregate neighborhood relations and provides high-quality latent codes. We then propose the invertible residual coupling stack in the flow model, in order to learn from the latent codes via an invertible manner. Based on the shape latent codes generated by the flow, the decoder converts the input noises into point clouds in an inverse way. Experimental results demonstrate that PointIVAE obtains the SOTA results in both point cloud generation and autoencoding.