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Perception of 3D object properties from 2D images form one of the core computer vision problems. In this work, we propose a deep learning system that can simultaneously reason about 3D shape as well as associated properties (such as color, semantic part segments) directly from a single 2D image. We devise a novel depth-aware differentiable feature rendering module (DIFFER) that is used to train our model by using only 2D supervision. Experiments on both synthetic ShapeNet dataset and thedblp:conf/cvpr/LMJB19 fatcat:djms5h3eavfcpk4qamxfr2le34