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The majority of extant works resort to\nregular representations such as volumetric grids or collection of images;\nhowever, these representations obscure the natural invariance of 3D shapes\nunder geometric transformations and also suffer from a number of other issues.\nIn this paper we address the problem of 3D reconstruction from a single image,\ngenerating a straight-forward form of output -- point cloud coordinates. Along\nwith this problem arises a unique and interesting issue, that the groundtruth\nshape for an input image may be ambiguous. Driven by this unorthodox output\nform and the inherent ambiguity in groundtruth, we design architecture, loss\nfunction and learning paradigm that are novel and effective. Our final solution\nis a conditional shape sampler, capable of predicting multiple plausible 3D\npoint clouds from an input image. 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