Pratul Srinivasan

I'm a research scientist at Google Research and an almost-graduated PhD student in the EECS Department at UC Berkeley, advised by Ren Ng and Ravi Ramamoorthi. I am part of the Berkeley AI Research (BAIR) lab. My main research interests lie at the intersection of computer vision, computer graphics, and machine learning.

During my PhD, I interned twice at Google Research: at Mountain View in 2017 (hosted by Jon Barron in Marc Levoy's group) and at New York City in 2018 (hosted by Noah Snavely).

I graduated from Duke University in 2014, where I majored in Biomedical Engineering and Computer Science. At Duke, I worked with Sina Farsiu on research problems in medical computer vision.

I grew up in Palo Alto, CA and graduated from Henry M. Gunn High School in 2010.

Email  /  CV  /  Google Scholar

Research and Publications

* denotes equal contribution co-authorship

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 Presentation)
project page / arXiv / code / bibtex

Mapping input coordinates with simple Fourier features before passing them to a fully-connected network enables the network to learn much higher-frequency functions.

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 (Oral Presentation, Best Paper Honorable Mention)
project page / arXiv / video / technical overview / code / two minute papers / bibtex

We optimize a simple neural network to represent a scene as a 5D function (3D volume + 2D view direction) from just a set of images, and synthesize photorealistic novel views.

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 / bibtex

We predict a multiscale light volume from an input stereo pair, and render this volume to compute illumination at any 3D point for relighting inserted virtual objects.

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
project page / arXiv / video / code / bibtex

We develop a deep learning method for rendering novel views of complex real world scenes from a small number of images, and analyze it with light field sampling theory.

Pushing the Boundaries of View Extrapolation with Multiplane Images
Pratul Srinivasan, Richard Tucker, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng, Noah Snavely
Computer Vision and Pattern Recognition (CVPR), 2019   (Oral Presentation, Best Paper Award Finalist)
arXiv / video / code / bibtex

We use Fourier theory to show the limits of view extrapolation with multiplane images, and develop a deep learning pipeline with 3D inpainting for better view extrapolation results.

Aperture Supervision for Monocular Depth Estimation
Pratul Srinivasan, Rahul Garg, Neal Wadhwa, Ren Ng, Jonathan T. Barron,
Computer Vision and Pattern Recognition (CVPR), 2018
arXiv / code / bibtex

We train a neural network to estimate a depth map from a single image using only images with different-sized apertures as supervision, and use this to synthesize artificial bokeh.

ChromaBlur: Rendering Chromatic Eye Aberration Improves Accommodation and Realism
Steven A. Cholewiak, Gordon D. Love, Pratul Srinivasan, Ren Ng, Martin S. Banks,
SIGGRAPH Asia, 2017  
project page / video / bibtex

We show that properly considering the eye's aberrations when rendering for VR displays increases perceived realism and helps drive accomodation.

Learning to Synthesize a 4D RGBD Light Field from a Single Image
Pratul Srinivasan, Tongzhou Wang, Ashwin Sreelal, Ravi Ramamoorthi, Ren Ng,
International Conference on Computer Vision (ICCV), 2017   (Spotlight Presentation)
arXiv / video / code / supplementary PDF / bibtex

We train a neural network to predict ray depths and RGB colors for a local light field around a single input image.

Light Field Blind Motion Deblurring
Pratul Srinivasan, Ren Ng, Ravi Ramamoorthi,
Conference Computer Vision and Pattern Recognition (CVPR), 2017   (Oral Presentation)
arXiv / video / code / additional results / bibtex

We develop Fourier theory to describe the effects of camera motion on light fields, and an optimization algorithm for deblurring light fields captured with unknown camera motion.

Oriented Light-Field Windows for Scene Flow
Pratul Srinivasan, Michael W. Tao, Ren Ng, Ravi Ramamoorthi,
International Conference on Computer Vision (ICCV), 2015
paper PDF / code / video / bibtex

We develop a 4D light field descriptor and an algorithm to use these to compute scene flow (3D motion of observed points) from two captured light fields.

Shape Estimation from Shading, Defocus, and Correspondence Using Light-Field Angular Coherence
Michael W. Tao, Pratul Srinivasan, Sunil Hadap, Szymon Rusinkiewicz, Jitendra Malik, Ravi Ramamoorthi,
IEEE Transactions on Pattern Matching and Machine Intelligence (PAMI), 2017 and Conference on Computer Vision and Pattern Recognition (CVPR), 2015
conference PDF / journal PDF / code / bibtex

We develop an algorithm that jointly considers cues from defocus, correspondence, and shading to estimate better depths from a light field.

Fully Automated Detection of Diabetic Macular Edema and Dry Age-Related Macular Degeneration from Optical Coherence Tomography Images
Pratul Srinivasan, Leo A. Kim, Priyatham S. Mettu, Scott W. Cousins, Grant M. Comer, Joseph A. Izatt, Sina Farsiu,
Biomedical Optics Express, 2014
journal article / dataset / bibtex

We develop a classification algorithm to detect diseases from OCT images of the retina.

Automatic Segmentation of up to Ten Layer Boundaries in SD-OCT Images of the Mouse Retina With and Without Missing Layers due to Pathology
Pratul Srinivasan, Stephanie J. Heflin, Joseph A. Izatt, Vadim Y. Arshavsky, Sina Farsiu,
Biomedical Optics Express, 2014
journal article / bibtex

We develop a segmentation algorithm to quantify the shape of retinal layers in OCT images that is robust to deformations due to disease.


CS184 - Computer Graphics and Imaging, Spring 2018 (GSI)

CS184 - Computer Graphics and Imaging, Spring 2019 (GSI)

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Last updated May 2020.