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3D reconstruction from single view images is an ill-posed problem. Inferringthe hidden regions from self-occluded images is both challenging and ambiguous.We propose a two-pronged approach to address these issues. To betterincorporate the data prior and generate meaningful reconstructions, we propose3D-LMNet, a latent embedding matching approach for 3D reconstruction. We firsttrain a 3D point cloud auto-encoder and then learn a mapping from the 2D imageto the corresponding learnt embedding. To tackle the issue of uncertainty inthe reconstruction, we predict multiple reconstructions that are consistentwith the input view. This is achieved by learning a probablistic latent spacewith a novel view-specific diversity loss. Thorough quantitative andqualitative analysis is performed to highlight the significance of the proposedapproach. We outperform state-of-the-art approaches on the task of single-view3D reconstruction on both real and synthetic datasets while generating multipleplausible reconstructions, demonstrating the generalizability and utility ofour approach.

3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image