Locally Orderless Images for Optimization in Differentiable Rendering
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
Problems in differentiable rendering often involve optimizing scene
parameters that cause motion in image space. The gradients for such parameters
tend to be sparse, leading to poor convergence. While existing methods address
this sparsity through proxy gradients such as topological derivatives or
lagrangian derivatives, they make simplifying assumptions about rendering.
Multi-resolution image pyramids offer an alternative approach but prove
unreliable in practice. We introduce a method that uses locally orderless
images, where each pixel maps to a histogram of intensities that preserves
local variations in appearance. Using an inverse rendering objective that
minimizes histogram distance, our method extends support for sparsely defined
image gradients and recovers optimal parameters. We validate our method on
various inverse problems using both synthetic and real data.
