2 citations · 3 across the 12 of their papers we have counts for
12 papers
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 Neural Antiderivatives
Fizza Rubab, Ntumba Elie Nsampi, Martin Balint +4
Neural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representatio…
MILO: A Lightweight Perceptual Quality Metric for Image and Latent-Space Optimization
Uğur Çoğalan, Mojtaba Bemana, Karol Myszkowski +2
We present MILO (Metric for Image- and Latent-space Optimization), a lightweight, multiscale, perceptual metric for full-reference image quality assessment (FR-IQA). MILO is traine…
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…
Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field
Chao Wang, Krzysztof Wolski, Bernhard Kerbl +5
Radiance field methods represent the state of the art in reconstructing complex scenes from multi-view photos. However, these reconstructions often suffer from one or both of the f…
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…
Enhancing image quality prediction with self-supervised visual masking
Uğur Çoğalan, Mojtaba Bemana, Hans-Peter Seidel +1
Full-reference image quality metrics (FR-IQMs) aim to measure the visual differences between a pair of reference and distorted images, with the goal of accurately predicting human…
Video frame interpolation for high dynamic range sequences captured with dual-exposure sensors
Uğur Çoğalan, Mojtaba Bemana, Hans-Peter Seidel +1
Video frame interpolation (VFI) enables many important applications that might involve the temporal domain, such as slow motion playback, or the spatial domain, such as stop motion…