Accelerating Inverse Rendering By Using a GPU and Reuse of Light Paths
This work addresses the slow iterative process in inverse rendering for computer graphics and imaging applications, offering an incremental improvement through better hardware utilization.
The paper tackles the computational inefficiency of inverse rendering algorithms by introducing a GPU-optimized framework with Path Sorting and Path Recycling, achieving significant speed-ups in gradient optimization for tasks like volumetric scattering tomography and reflectometry.
Inverse rendering seeks to estimate scene characteristics from a set of data images. The dominant approach is based on differential rendering using Monte-Carlo. Algorithms as such usually rely on a forward model and use an iterative gradient method that requires sampling millions of light paths per iteration. This paper presents an efficient framework that speeds up existing inverse rendering algorithms. This is achieved by tailoring the iterative process of inverse rendering specifically to a GPU architecture. For this cause, we introduce two interleaved steps - Path Sorting and Path Recycling. Path Sorting allows the GPU to deal with light paths of the same size. Path Recycling allows the algorithm to use light paths from previous iterations to better utilize the information they encode. Together, these steps significantly speed up gradient optimization. In this paper, we give the theoretical background for Path Recycling. We demonstrate its efficiency for volumetric scattering tomography and reflectometry (surface reflections).