2 citations · 3 across the 12 of their papers we have counts for
7 papers · 1 filter
Volumetric Inverse Rendering via Neural Radiative Transfer
Ntumba Elie Nsampi, Adarsh Djeacoumar, Hans-Peter Seidel +2
Volumetric inverse rendering seeks to recover the optical properties of participating media from images. Existing approaches either rely on differentiable stochastic light transpor…
Forget Superresolution, Sample Adaptively (when Path Tracing)
Martin Bálint, Corentin Salaün, Hans-Peter Seidel +1
Real-time path tracing increasingly operates under extremely low sampling budgets, often below one sample per pixel, as rendering complexity, resolution, and frame-rate requirement…
Learning Image Fractals Using Chaotic Differentiable Point Splatting
Adarsh Djeacoumar, Felix Mujkanovic, Hans-Peter Seidel +1
Fractal geometry, defined by self-similar patterns across scales, is crucial for understanding natural structures. This work addresses the fractal inverse problem, which involves e…
Jump Restore Light Transport
Sascha Holl, Gurprit Singh, Hans-Peter Seidel
Markov chain Monte Carlo (MCMC) algorithms are indispensable when sampling from a complex, high-dimensional distribution by a conventional method is intractable. Even though MCMC i…
Learning Images Across Scales Using Adversarial Training
Krzysztof Wolski, Adarsh Djeacoumar, Alireza Javanmardi +7
The real world exhibits rich structure and detail across many scales of observation. It is difficult, however, to capture and represent a broad spectrum of scales using ordinary im…
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising
Mojtaba Bemana, Thomas Leimkühler, Karol Myszkowski +2
We demonstrate generating HDR images using the concerted action of multiple black-box, pre-trained LDR image diffusion models. Relying on a pre-trained LDR generative diffusion mod…
Joint Sampling and Optimisation for Inverse Rendering
Martin Balint, Karol Myszkowski, Hans-Peter Seidel +1
When dealing with difficult inverse problems such as inverse rendering, using Monte Carlo estimated gradients to optimise parameters can slow down convergence due to variance. Aver…