Differentiable Inverse Rendering for Material Estimation

Project Description:

In this project, we will apply differentiable inverse rendering (DIR) with the aim of reconstructing material properties for observed objects. We will conduct experiments with both synthetic and real-world data starting with simple conditions and gradually increasing the level of complexity. For example, we will start practicing material estimation for objects with known geometry and homogenous surface properties and will later transition to estimation of spatially varying BRDFs for objects of unknown geometry.

We would like to answer the questions like the following:

Which BRDF representations are differentiable and why?
What are the benefits and drawbacks of different geometric surface representations with respect to differentiable inverse rendering?
What is the best tradeoff between fast and realistic (physically based) rendering to speed up the optimization process?

Prerequisites:

• successful completion of a Computer Graphics course and Image Analysis or Computer Vision course
• solid programing skills in C/C++ or Python
• desire to work in a team