most citedHuman Vision Constrained Super-Resolution

1 citations · 1 across the 2 of their papers we have counts for

collaborators

11 papers

cs.CV20261 cited

Human Vision Constrained Super-Resolution

Volodymyr Karpenko, Taimoor Tariq, Jorge Condor +1

Modern deep-learning super-resolution (SR) techniques process images and videos independently of the underlying content and viewing conditions. However, the sensitivity of the huma…

cs.CV2026

Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstruction

Jorge Condor, Nicolas Moenne-Loccoz, Merlin Nimier-David +3

Primitive-based methods such as 3D Gaussian Splatting have recently become the state-of-the-art for novel-view synthesis and related reconstruction tasks. Compared to neural fields…

cs.CV2026

Beyond Spherical Harmonics: Rethinking Appearance Models for Radiance Reconstruction

Ewa Miazga, Jorge Condor, Piotr Didyk

View-dependent appearance modeling remains a challenging problem in novel-view synthesis and reconstruction. Accurately representing complex angular effects often requires substant…

cs.CL2026

The Cost of Language: Centroid Erasure Exposes and Exploits Modal Competition in Multimodal Language Models

Akshay Paruchuri, Ishan Chatterjee, Henry Fuchs +2

Multimodal language models systematically underperform on visual perception tasks, yet the structure underlying this failure remains poorly understood. We propose centroid replacem…

cs.GR2026

Gabor Fields: Orientation-Selective Level-of-Detail for Volume Rendering

Jorge Condor, Nicolai Hermann, Mehmet Ata Yurtsever +1

Gaussian-based representations have enabled efficient physically-based volume rendering at a fraction of the memory cost of regular, discrete, voxel-based distributions. However, s…

cs.GR2026

It's Not Just a Phase: Creating Phase-Aligned Peripheral Metamers

Sophie Kergaßner, Piotr Didyk

Novel display technologies can deliver high-quality images across a wide field of view, creating immersive experiences. While rendering for such devices is expensive, most of the c…