Pratul Srinivasan

I'm a research scientist at Google Research. My research interests lie at the intersection of computer vision, computer graphics, and machine learning.

I received my PhD from the EECS Department at UC Berkeley in December 2020, where I was advised by Ren Ng and Ravi Ramamoorthi. At UC Berkeley, I was a member of the Berkeley AI Research (BAIR) lab.

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  /  Twitter

Research and Publications

* denotes equal contribution co-authorship

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
ICCV, 2021 (Oral Presentation)
project page / arXiv / video / bibtex

We modify NeRF to output volume density and emitted radiance at a volume of space instead of a single point to fix NeRF's issues with sampling and aliasing.

Baking Neural Radiance Fields for Real-Time View Synthesis
Peter Hedman, Pratul Srinivasan , Ben Mildenhall, Jonathan T. Barron, Paul Debevec
ICCV, 2021 (Oral Presentation)
project page / arXiv / video / demo / bibtex

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

NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
Xiuming Zhang, Pratul Srinivasan, Boyang Deng, Paul Debevec, William T. Freeman, Jonathan T. Barron
arXiv, 2021
project page / video / arXiv / bibtex

We recover relightable NeRF-like models from images under a single unknown lighting condition.

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

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

Learned Initializations for Optimizing Coordinate-Based Neural Representations
Matthew Tancik*, Ben Mildenhall*, Terrance Wang, Divi Schmidt, Pratul Srinivasan,
Jonathan T. Barron, Ren Ng
CVPR, 2021 (Oral Presentation)
project page / video / arXiv / bibtex

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

IBRNet: Learning Multi-View Image-Based Rendering
Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou,
Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser
CVPR, 2021
project page / arXiv / bibtex

Training a network that blends source views using a NeRF-like continuous neural volumetric representation, for NeRF-like performance without per-scene 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 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.

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 / bibtex

We recover relightable NeRF-like models by predicting per-location BRDFs and surface normals, and marching light rays through the NeRF 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 (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.

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 / bibtex

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 / 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.