Search Results for author: Jacob Munkberg

Found 3 papers, 3 papers with code

Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising

1 code implementation7 Jun 2022 Jon Hasselgren, Nikolai Hofmann, Jacob Munkberg

Unfortunately, Monte Carlo integration provides estimates with significant noise, even at large sample counts, which makes gradient-based inverse rendering very challenging.

3D Reconstruction Denoising

Extracting Triangular 3D Models, Materials, and Lighting From Images

2 code implementations CVPR 2022 Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, Sanja Fidler

We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations.

Noise2Noise: Learning Image Restoration without Clean Data

20 code implementations ICML 2018 Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.

BIG-bench Machine Learning Image Restoration +1

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