  We propose SDFDiff, a novel approach for image-based shape optimization using
differentiable rendering of 3D shapes represented by signed distance functions
(SDFs). Compared to other representations, SDFs have the advantage that they
can represent shapes with arbitrary topology, and that they guarantee
watertight surfaces. We apply our approach to the problem of multi-view 3D
reconstruction, where we achieve high reconstruction quality and can capture
complex topology of 3D objects. In addition, we employ a multi-resolution
strategy to obtain a robust optimization algorithm. We further demonstrate that
our SDF-based differentiable renderer can be integrated with deep learning
models, which opens up options for learning approaches on 3D objects without 3D
supervision. In particular, we apply our method to single-view 3D
reconstruction and achieve state-of-the-art results.
