Search Results for author: Pratul P. Srinivasan

Found 17 papers, 9 papers with code

Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

no code implementations ICCV 2021 Shumian Xin, Neal Wadhwa, Tianfan Xue, Jonathan T. Barron, Pratul P. Srinivasan, Jiawen Chen, Ioannis Gkioulekas, Rahul Garg

We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.


NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination

1 code implementation3 Jun 2021 Xiuming Zhang, Pratul P. Srinivasan, Boyang Deng, Paul Debevec, William T. Freeman, Jonathan T. Barron

The key to our approach, which we call Neural Radiance Factorization (NeRFactor), is to distill the volumetric geometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020] representation of the object into a surface representation and then jointly refine the geometry while solving for the spatially-varying reflectance and the environment lighting.

Baking Neural Radiance Fields for Real-Time View Synthesis

no code implementations ICCV 2021 Peter Hedman, Pratul P. Srinivasan, Ben Mildenhall, Jonathan T. Barron, Paul Debevec

Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged as a compelling technique for learning to represent 3D scenes from images with the goal of rendering photorealistic images of the scene from unobserved viewpoints.

Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields

1 code implementation ICCV 2021 Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, Pratul P. Srinivasan

Mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.

NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis

no code implementations CVPR 2021 Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron

We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions.

Learned Initializations for Optimizing Coordinate-Based Neural Representations

2 code implementations CVPR 2021 Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng

Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals.


Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

8 code implementations NeurIPS 2020 Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng

We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains.

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

21 code implementations ECCV 2020 Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng

Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x, y, z)$ and viewing direction $(\theta, \phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location.

Neural Rendering Novel View Synthesis

Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination

1 code implementation CVPR 2020 Pratul P. Srinivasan, Ben Mildenhall, Matthew Tancik, Jonathan T. Barron, Richard Tucker, Noah Snavely

We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair.

Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines

1 code implementation2 May 2019 Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, Abhishek Kar

We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration.

Novel View Synthesis

Pushing the Boundaries of View Extrapolation with Multiplane Images

1 code implementation CVPR 2019 Pratul P. Srinivasan, Richard Tucker, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng, Noah Snavely

We present a theoretical analysis showing how the range of views that can be rendered from an MPI increases linearly with the MPI disparity sampling frequency, as well as a novel MPI prediction procedure that theoretically enables view extrapolations of up to $4\times$ the lateral viewpoint movement allowed by prior work.

Aperture Supervision for Monocular Depth Estimation

no code implementations CVPR 2018 Pratul P. Srinivasan, Rahul Garg, Neal Wadhwa, Ren Ng, Jonathan T. Barron

We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision.

Monocular Depth Estimation

Learning to Synthesize a 4D RGBD Light Field from a Single Image

1 code implementation ICCV 2017 Pratul P. Srinivasan, Tongzhou Wang, Ashwin Sreelal, Ravi Ramamoorthi, Ren Ng

We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction).

Depth Estimation

Light Field Blind Motion Deblurring

no code implementations CVPR 2017 Pratul P. Srinivasan, Ren Ng, Ravi Ramamoorthi

We study the problem of deblurring light fields of general 3D scenes captured under 3D camera motion and present both theoretical and practical contributions.


Oriented Light-Field Windows for Scene Flow

no code implementations ICCV 2015 Pratul P. Srinivasan, Michael W. Tao, Ren Ng, Ravi Ramamoorthi

2D spatial image windows are used for comparing pixel values in computer vision applications such as correspondence for optical flow and 3D reconstruction, bilateral filtering, and image segmentation.

3D Reconstruction Optical Flow Estimation +2

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