  Reconstructing the shape and appearance of real-world objects using measured
2D images has been a long-standing problem in computer vision. In this paper,
we introduce a new analysis-by-synthesis technique capable of producing
high-quality reconstructions through robust coarse-to-fine optimization and
physics-based differentiable rendering.
  Unlike most previous methods that handle geometry and reflectance largely
separately, our method unifies the optimization of both by leveraging image
gradients with respect to both object reflectance and geometry. To obtain
physically accurate gradient estimates, we develop a new GPU-based Monte Carlo
differentiable renderer leveraging recent advances in differentiable rendering
theory to offer unbiased gradients while enjoying better performance than
existing tools like PyTorch3D and redner. To further improve robustness, we
utilize several shape and material priors as well as a coarse-to-fine
optimization strategy to reconstruct geometry. We demonstrate that our
technique can produce reconstructions with higher quality than previous methods
such as COLMAP and Kinect Fusion.
