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

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