Jonathan T. Barron
Also known as Jon Barron, Jonathan Barron
Jonathan T. Barron researches computer vision, image formation and neural scene representations. His work includes SIRFS with Jitendra Malik, which estimates shape, lighting and reflectance from an image. He later coauthored mip-NeRF and Zip-NeRF, methods that improve the quality and efficiency of neural radiance fields. He received the PAMI Young Researcher Award in 2020. As of October 2026, he leads a small Google DeepMind team focused mainly on NeRF.
- Now at
- Google DeepMind
- Works
- 141
- Citations
- 39,466
- h-index
- 68
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
-
2015
Recovering scene properties
Barron and Jitendra Malik published work on shape, illumination and reflectance from shading. The method estimates several physical properties from a single image of an object. This fits Barron’s broader research on inferring the physical world from pictures.
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2020
Computer vision recognition
Jon Barron received the PAMI Young Researcher Award in 2020.
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2021
Reducing rendering artifacts
Barron coauthored mip-NeRF, a multiscale neural radiance-field method. It uses cone-shaped regions and a filtered encoding to reduce aliasing. This addresses the artifacts that can appear when a scene model is viewed at different image scales.
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2023
Faster scene models
He coauthored Zip-NeRF, which combines ideas from multiscale radiance fields and grid-based scene models. The method improves rendering quality and speeds training in the tested setting.
Sources and credits
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