Search Results for author: Jacob Munkberg

Found 6 papers, 3 papers with code

Noise2Noise: Learning Image Restoration without Clean Data

21 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

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 +5

Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes

no code implementations6 Apr 2023 Zian Wang, Tianchang Shen, Jun Gao, Shengyu Huang, Jacob Munkberg, Jon Hasselgren, Zan Gojcic, Wenzheng Chen, Sanja Fidler

Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion.

3D Reconstruction Inverse Rendering

Flexible Isosurface Extraction for Gradient-Based Mesh Optimization

no code implementations10 Aug 2023 Tianchang Shen, Jacob Munkberg, Jon Hasselgren, Kangxue Yin, Zian Wang, Wenzheng Chen, Zan Gojcic, Sanja Fidler, Nicholas Sharp, Jun Gao

This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative modeling, and inverse physics.

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