  Modern computer vision algorithms have brought significant advancement to 3D
geometry reconstruction. However, illumination and material reconstruction
remain less studied, with current approaches assuming very simplified models
for materials and illumination. We introduce Inverse Path Tracing, a novel
approach to jointly estimate the material properties of objects and light
sources in indoor scenes by using an invertible light transport simulation. We
assume a coarse geometry scan, along with corresponding images and camera
poses. The key contribution of this work is an accurate and simultaneous
retrieval of light sources and physically based material properties (e.g.,
diffuse reflectance, specular reflectance, roughness, etc.) for the purpose of
editing and re-rendering the scene under new conditions. To this end, we
introduce a novel optimization method using a differentiable Monte Carlo
renderer that computes derivatives with respect to the estimated unknown
illumination and material properties. This enables joint optimization for
physically correct light transport and material models using a tailored
stochastic gradient descent.
