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.
	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.
	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.