3DMaskGAN:Unsupervised SingleView 3D Object Reconstruction 3D object reconstruction has always been a hot topic in computer vision. Especially in recent years, many methods of learning volumetric predictions achieve robust 3D reconstruction using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, the majority of methods employ strong shape priors and exist computationally waste in predicting 3D shapes. In this paper, we propose 3DMaskGAN, a novel framework to efficiently accomplish the task of unsupervised singleview 3D object reconstruction. We use 3D Generative Adversarial Networks (GAN) to predict the 3D shape from the singleview image and improve reconstruction accuracy by applying 2D projection masks instead of 3D priors simultaneously. The key idea is to insert a projector (a buildin camera system to approximate the true rendering pipeline) into the framework of 3D GAN to synthesize novel masks for optimization. We learn singleclass and multiclass objects to evaluate our network. Experimental results show that our framework achieves impressive performance with fewer training iterations in terms of unsupervised shape predictions.

Representation Learning via Parallel Subset Reconstruction for 3D Point Cloud Generation Threedimensional (3D) point cloud processing has attracted a great deal of attention in computer vision, robotics, and the machine learning community because of significant progress in deep neural networks on 3D data. Another trend in the community is learning of generative models based on generative adversarial networks. In this paper, we propose a framework for 3D point cloud generation based on a combination of autoencoders and generative adversarial networks. The framework first trains autoencoders to learn latent representations, and then trains generative adversarial networks in the learned latent space. We focus on improving the training method for autoencoders in order to generate 3D point clouds with higher fidelity and coverage. We add parallel subdecoders that reconstruct subsets of the input point cloud. In order to construct these subsets, we introduce a point sampling algorithm that imposes a method to sample spatially localized point sets. These local subsets are utilized to measure local reconstruction losses. We train autoencoders to learn an effective latent representation for both global and local shape reconstruction based on the multitask learning approach. Furthermore, we add global and local adversarial losses to generate more plausible point clouds. Quantitative and qualitative evaluations demonstrate that the proposed method outperforms stateoftheart method on the task of 3D point cloud generation.