Target-Aware Image Denoising for Inverse Monte Carlo Rendering

Jeongmin Gu 1 , Jonghee Back 1 , Sung-Eui Yoon 2 , Bochang Moon 1

Gwangju Institute of Science and Technology 1 , KAIST 2

ACM Transactions on Graphics (SIGGRAPH 2024)

Target-Aware Image Denoising for Inverse Monte Carlo Rendering
Optimization results where we use a gradient-based optimizer that infers the scene parameters (i.e., textures within the yellow box) from its initial (a reddish one) so that the rendered image with the inferred textures is close to the target image. We compare the images rendered using the parameters inferred by the inverse rendering optimization without and with image denoising, i.e., the baseline and two denoisers (a cross-bilateral filter and our denoiser). Adopting an existing denoiser (i.e., the cross-bilateral filter) allows faster convergence than the baseline without image denoising, but the optimization goes into an undesirable local minimum, i.e., over-blurred textures. On the other hand, our image denoiser makes the scene inference robust by guiding the optimizer to preserve the texture details.



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