activity
20192025
most citedHandheld Multi-Frame Super-Resolution

214 citations · 258 across the 12 of their papers we have counts for

collaborators

12 papers

cs.GR2025

A Generalizable Light Transport 3D Embedding for Global Illumination

Bing Xu, Mukund Varma T, Cheng Wang +5

Global illumination (GI) is essential for realistic rendering but remains computationally expensive due to the complexity of simulating indirect light transport. Recent neural meth…

cs.GR2025

Collaborative Texture Filtering

Tomas Akenine-Möller, Pontus Ebelin, Matt Pharr +1

Recent advances in texture compression provide major improvements in compression ratios, but cannot use the GPU's texture units for decompression and filtering. This has led to the…

cs.GR2025★ 1 cited

Improved Stochastic Texture Filtering Through Sample Reuse

Bartlomiej Wronski, Matt Pharr, Tomas Akenine-Möller

Stochastic texture filtering (STF) has re-emerged as a technique that can bring down the cost of texture filtering of advanced texture compression methods, e.g., neural texture com…

cs.GR2025★ 1 cited

GPU-Friendly Laplacian Texture Blending

Bartlomiej Wronski

Texture and material blending is one of the leading methods for adding variety to rendered virtual worlds, creating composite materials, and generating procedural content. When don…

cs.GR2025★ 3 cited

Generative Detail Enhancement for Physically Based Materials

Saeed Hadadan, Benedikt Bitterli, Tizian Zeltner +6

We present a tool for enhancing the detail of physically based materials using an off-the-shelf diffusion model and inverse rendering. Our goal is to enhance the visual fidelity of…

cs.CV2024★ 6 cited

Filtering After Shading With Stochastic Texture Filtering

Matt Pharr, Bartlomiej Wronski, Marco Salvi +1

2D texture maps and 3D voxel arrays are widely used to add rich detail to the surfaces and volumes of rendered scenes, and filtered texture lookups are integral to producing high-q…

cs.GR2023★ 29 cited

Random-Access Neural Compression of Material Textures

Karthik Vaidyanathan, Marco Salvi, Bartlomiej Wronski +3

The continuous advancement of photorealism in rendering is accompanied by a growth in texture data and, consequently, increasing storage and memory demands. To address this issue,…

cs.GR2023

Stochastic Texture Filtering

Marcos Fajardo, Bartlomiej Wronski, Marco Salvi +1

2D texture maps and 3D voxel arrays are widely used to add rich detail to the surfaces and volumes of rendered scenes, and filtered texture lookups are integral to producing high-q…

cs.CV2022★ 1 cited

Fast and High-Quality Image Denoising via Malleable Convolutions

Yifan Jiang, Bartlomiej Wronski, Ben Mildenhall +3

Most image denoising networks apply a single set of static convolutional kernels across the entire input image. This is sub-optimal for natural images, as they often consist of het…

cs.CV2021★ 1 cited

Procedural Kernel Networks

Bartlomiej Wronski

In the last decade Convolutional Neural Networks (CNNs) have defined the state of the art for many low level image processing and restoration tasks such as denoising, demosaicking,…

cs.GR2020★ 2 cited

Image Stylization: From Predefined to Personalized

Ignacio Garcia-Dorado, Pascal Getreuer, Bartlomiej Wronski +1

We present a framework for interactive design of new image stylizations using a wide range of predefined filter blocks. Both novel and off-the-shelf image filtering and rendering t…

cs.CV2019★ 214 cited

Handheld Multi-Frame Super-Resolution

Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst +5

Compared to DSLR cameras, smartphone cameras have smaller sensors, which limits their spatial resolution; smaller apertures, which limits their light gathering ability; and smaller…