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Dense 3D object reconstruction from a single image has recently witnessedremarkable advances, but supervising neural networks with ground-truth 3Dshapes is impractical due to the laborious process of creating pairedimage-shape datasets. Recent efforts have turned to learning 3D reconstructionwithout 3D supervision from RGB images with annotated 2D silhouettes,dramatically reducing the cost and effort of annotation. These techniques,however, remain impractical as they still require multi-view annotations of thesame object instance during training. As a result, most experimental efforts todate have been limited to synthetic datasets. In this paper, we address thisissue and propose SDF-SRN, an approach that requires only a single view ofobjects at training time, offering greater utility for real-world scenarios.SDF-SRN learns implicit 3D shape representations to handle arbitrary shapetopologies that may exist in the datasets. To this end, we derive a noveldifferentiable rendering formulation for learning signed distance functions(SDF) from 2D silhouettes. Our method outperforms the state of the art underchallenging single-view supervision settings on both synthetic and real-worlddatasets.

SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images