Ben Mildenhall

I am a research scientist at Google Research, where I work on problems in computer vision and graphics. I recently received my PhD from UC Berkeley, where I was advised by Ren Ng and supported by a Hertz fellowship.

In the summer of 2017, I was an intern in Marc Levoy's group in Google Research. In the summer of 2018, I worked with Rodrigo Ortiz-Cayon and Abhishek Kar at Fyusion. I did my undergrad at Stanford University and worked at Pixar Research in the summer of 2014.

Email  /  CV  /  Google Scholar  /  Twitter  /  GitHub

Research

Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, Pratul Srinivasan
International Conference on Computer Vision (ICCV), 2021 (oral)
project page / arXiv / video

We prefilter the positional encoding function and train NeRF to generate anti-aliased renderings.

Baking Neural Radiance Fields for Real-Time View Synthesis
Peter Hedman, Pratul Srinivasan, Ben Mildenhall, Jonathan T. Barron, Paul Debevec
International Conference on Computer Vision (ICCV), 2021 (oral)
project page / video / demo

We bake a trained NeRF into a sparse voxel grid of colors and features in order to render it in real-time.

Learned Initializations for Optimizing Coordinate-Based Neural Representations
Matthew Tancik*, Ben Mildenhall*, Terrance Wang, Divi Schmidt, Pratul Srinivasan, Jonathan T. Barron, Ren Ng
Computer Vision and Pattern Recognition (CVPR), 2021 (oral)
project page / arXiv / video / code

We use meta-learning to find weight initializations for coordinate-based MLPs that allow them to converge faster and generalize better.

NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis
Pratul Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron
Computer Vision and Pattern Recognition (CVPR), 2021
project page / arXiv / video

We recover relightable NeRF-like models using neural approximations of expensive visibility integrals, so we can simulate complex volumetric light transport during training.

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik*, Pratul Srinivasan*, Ben Mildenhall*, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng
NeurIPS, 2020 (spotlight)
project page / arXiv / code

We demonstrate that composing fully-connected networks with a simple Fourier feature mapping allows them to learn much high frequency functions.

Neural Reflectance Fields for Appearance Acquisition
Sai Bi*, Zexiang Xu*, Pratul Srinivasan, Ben Mildenhall, Kalyan Sunkavalli, Milos Hasan, Yannick Hold-Geoffroy, David Kriegman, Ravi Ramamoorthi
arXiv, 2020
arXiv

We recover relightable NeRF-like models by predicting per-location BRDFs and surface normals, and marching light rays through the NeRV volume to compute visibility.

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Ben Mildenhall*, Pratul Srinivasan*, Matthew Tancik*, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng
European Conference on Computer Vision (ECCV), 2020 (Best Paper Honorable Mention)
project page / arXiv / video / talk / code / two minute papers / papers with code

We optimize a simple fully-connected network to represent a single scene as a volume, then use volume rendering to do view synthesis.

Deep Multi Depth Panoramas for View Synthesis
Kai-En Lin, Zexiang Xu, Ben Mildenhall, Pratul Srinivasan, Yannick Hold-Geoffroy, Stephen DiVerdi, Qi Sun, Kalyan Sunkavalli, Ravi Ramamoorthi
European Conference on Computer Vision (ECCV), 2020
arXiv / video

We represent scenes as multi-layer panoramas with depth for VR view synthesis.

Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
Pratul Srinivasan*, Ben Mildenhall*, Matthew Tancik, Jonathan T. Barron, Richard Tucker, Noah Snavely
Computer Vision and Pattern Recognition (CVPR), 2020
project page / arXiv / video / code

We predict a volume from an input stereo pair that can be used to calculate incident lighting at any 3D point within a scene.

StegaStamp: Invisible Hyperlinks in Physical Photographs
Matthew Tancik*, Ben Mildenhall*, Ren Ng
Computer Vision and Pattern Recognition (CVPR), 2020
project page / arXiv / video / code

We can hide hyperlinks in natural images to create aesthetically pleasing barcodes.

Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
Ben Mildenhall*, Pratul Srinivasan*, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, Abhishek Kar
SIGGRAPH, 2019
project page / arXiv / video / code

We develop and analyze a deep learning method for rendering novel views of complex real world scenes.

Unprocessing Images for Learned Raw Denoising
Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, Jonathan T. Barron
Computer Vision and Pattern Recognition (CVPR), 2019 (oral)
project page / arXiv

We can learn a better denoising model by processing and unprocessing images the same way a camera does.

Burst Denoising with Kernel Prediction Networks
Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, Robert Carroll
Computer Vision and Pattern Recognition (CVPR), 2018 (spotlight)
project page / arXiv / code

We train a network to predict linear kernels that denoise bursts of raw linear images.

DiffuserCam: Lensless Single-exposure 3D Imaging
Nick Antipa, Grace Kuo, Reinhard Heckel, Ben Mildenhall, Emrah Bostan, Ren Ng, Laura Waller
Optica, 2018
project page / arXiv

Using a diffuser instead of a lens lets you recover 3D in a single exposure.

microflakes

Approximations for the distribution of microflake normals
Nelson Max, Tom Duff, Ben Mildenhall, Yajie Yan
The Visual Computer, 2017

We precompute microflake approximations to make rendering large meshes at a distance more efficient.

sosmc

Controlling Procedural Modeling Programs with Stochastically-Ordered Sequential Monte Carlo
Daniel Ritchie, Ben Mildenhall, Noah D. Goodman, Pat Hanrahan
SIGGRAPH, 2015

We improve control over the output of highly-variable procedural modeling programs by using SOSMC to provide incremental feedback on partially-generated models.

Course Projects
prl

Extending the PBRT Renderer to Support Volumetric Light Sources
Grand prize in CS384B rendering competition, Spring 2013


Adding support for multicolored, nonhomogeneous, emissive volumes in PBRT 2's path tracing integrator.

prl

Ray Tracer
CS148 assignment, Fall 2012


Implemented a ray tracer with reflections, refractions, soft shadows, and depth of field.

Teaching
dragon

CS184 - Summer 2020 (Co-instructor)

CS184 - Spring 2017 (GSI)

CS184 - Spring 2016 (GSI)

Music
piano

Rachmaninoff - Etude-Tableau Op. 39 No. 5
Encore after a salon recital, Spring 2019

Rachmaninoff - Rhapsody on a Theme of Paganini
With Stanford Symphony Orchestra, Spring 2015

Brahms - Piano Quintet
Recital at Stanford, Winter 2015


Yep it's another Jon Barron website.
Last updated March 2021.