SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
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 …