1 code implementation • ECCV 2020 • Jonathan T. Barron
We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding.
no code implementations • 19 Feb 2024 • Christian Reiser, Stephan Garbin, Pratul P. Srinivasan, Dor Verbin, Richard Szeliski, Ben Mildenhall, Jonathan T. Barron, Peter Hedman, Andreas Geiger
Third, we minimize the binary entropy of the opacity values, which facilitates the extraction of surface geometry by encouraging opacity values to binarize towards the end of training.
no code implementations • 18 Jan 2024 • Andreas Engelhardt, Amit Raj, Mark Boss, Yunzhi Zhang, Abhishek Kar, Yuanzhen Li, Deqing Sun, Ricardo Martin Brualla, Jonathan T. Barron, Hendrik P. A. Lensch, Varun Jampani
We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background.
no code implementations • 12 Dec 2023 • Daniel Duckworth, Peter Hedman, Christian Reiser, Peter Zhizhin, Jean-François Thibert, Mario Lučić, Richard Szeliski, Jonathan T. Barron
Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed, and modern methods are capable of rendering near-photorealistic scenes at interactive frame rates.
no code implementations • 11 Dec 2023 • Pratul P. Srinivasan, Stephan J. Garbin, Dor Verbin, Jonathan T. Barron, Ben Mildenhall
We present a UV mapping method designed to operate on geometry produced by 3D reconstruction and generation techniques.
no code implementations • 5 Dec 2023 • Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, Aleksander Holynski
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes.
no code implementations • 11 Oct 2023 • Ryan Po, Wang Yifan, Vladislav Golyanik, Kfir Aberman, Jonathan T. Barron, Amit H. Bermano, Eric Ryan Chan, Tali Dekel, Aleksander Holynski, Angjoo Kanazawa, C. Karen Liu, Lingjie Liu, Ben Mildenhall, Matthias Nießner, Björn Ommer, Christian Theobalt, Peter Wonka, Gordon Wetzstein
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes.
no code implementations • 21 Aug 2023 • Keunhong Park, Philipp Henzler, Ben Mildenhall, Jonathan T. Barron, Ricardo Martin-Brualla
We propose using a proxy problem to compute a whitening transform that eliminates the correlation between camera parameters and normalizes their effects, and we propose to use this transform as a preconditioner for the camera parameters during joint optimization.
no code implementations • 25 May 2023 • Dor Verbin, Ben Mildenhall, Peter Hedman, Jonathan T. Barron, Todd Zickler, Pratul P. Srinivasan
We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatially-varying materials, the surrounding illumination environment, and the shapes of the unseen light occluders who inadvertently cast shadows upon it.
no code implementations • 27 Apr 2023 • Guandao Yang, Abhijit Kundu, Leonidas J. Guibas, Jonathan T. Barron, Ben Poole
Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data.
1 code implementation • ICCV 2023 • Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, Peter Hedman
Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density.
no code implementations • 28 Feb 2023 • Lior Yariv, Peter Hedman, Christian Reiser, Dor Verbin, Pratul P. Srinivasan, Richard Szeliski, Jonathan T. Barron, Ben Mildenhall
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis.
no code implementations • 23 Feb 2023 • Christian Reiser, Richard Szeliski, Dor Verbin, Pratul P. Srinivasan, Ben Mildenhall, Andreas Geiger, Jonathan T. Barron, Peter Hedman
We design a lossless procedure for baking the parameterization used during training into a model that achieves real-time rendering while still preserving the photorealistic view synthesis quality of a volumetric radiance field.
1 code implementation • 9 Feb 2023 • Guandao Yang, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie
We use this framework to design Fourier PNFs, which match state-of-the-art performance in signal representation tasks that use neural fields.
no code implementations • CVPR 2023 • Yifan Jiang, Peter Hedman, Ben Mildenhall, Dejia Xu, Jonathan T. Barron, Zhangyang Wang, Tianfan Xue
Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function.
4 code implementations • 29 Sep 2022 • Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall
Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss.
Ranked #5 on Text to 3D on T$^3$Bench
1 code implementation • 31 May 2022 • Mark Boss, Andreas Engelhardt, Abhishek Kar, Yuanzhen Li, Deqing Sun, Jonathan T. Barron, Hendrik P. A. Lensch, Varun Jampani
Our method works on in-the-wild online image collections of an object and produces relightable 3D assets for several use-cases such as AR/VR.
no code implementations • 3 Mar 2022 • Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Tsung-Yi Lin, Alberto Rodriguez, Phillip Isola
In particular, we demonstrate that a NeRF representation of a scene can be used to train dense object descriptors.
2 code implementations • CVPR 2022 • Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P. Srinivasan, Jonathan T. Barron, Henrik Kretzschmar
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments.
1 code implementation • CVPR 2022 • Chung-Yi Weng, Brian Curless, Pratul P. Srinivasan, Jonathan T. Barron, Ira Kemelmacher-Shlizerman
Our method optimizes for a volumetric representation of the person in a canonical T-pose, in concert with a motion field that maps the estimated canonical representation to every frame of the video via backward warps.
no code implementations • 2 Jan 2022 • Yifan Jiang, Bartlomiej Wronski, Ben Mildenhall, Jonathan T. Barron, Zhangyang Wang, Tianfan Xue
These spatially-varying kernels are produced by an efficient predictor network running on a downsampled input, making them much more efficient to compute than per-pixel kernels produced by a full-resolution image, and also enlarging the network's receptive field compared with static kernels.
1 code implementation • 22 Dec 2021 • Jonathan T. Barron
We present squareplus, an activation function that resembles softplus, but which can be computed using only algebraic operations: addition, multiplication, and square-root.
2 code implementations • CVPR 2022 • Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T. Barron, Pratul P. Srinivasan
Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provide the volume density and view-dependent emitted radiance at each location.
1 code implementation • CVPR 2022 • Barbara Roessle, Jonathan T. Barron, Ben Mildenhall, Pratul P. Srinivasan, Matthias Nießner
To this end, we leverage dense depth priors in order to constrain the NeRF optimization.
4 code implementations • CVPR 2022 • Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole
Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision.
no code implementations • CVPR 2022 • Michael Niemeyer, Jonathan T. Barron, Ben Mildenhall, Mehdi S. M. Sajjadi, Andreas Geiger, Noha Radwan
We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training.
no code implementations • CVPR 2022 • Konstantinos Rematas, Andrew Liu, Pratul P. Srinivasan, Jonathan T. Barron, Andrea Tagliasacchi, Thomas Funkhouser, Vittorio Ferrari
The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e. g., Street View).
1 code implementation • CVPR 2022 • Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T. Barron
By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks.
1 code implementation • CVPR 2022 • Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, Peter Hedman
Though neural radiance fields (NeRF) have demonstrated impressive view synthesis results on objects and small bounded regions of space, they struggle on "unbounded" scenes, where the camera may point in any direction and content may exist at any distance.
1 code implementation • 10 Nov 2021 • Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Niessner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhoefer, Vladislav Golyanik
The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering.
1 code implementation • NeurIPS 2021 • Mark Boss, Varun Jampani, Raphael Braun, Ce Liu, Jonathan T. Barron, Hendrik P. A. Lensch
Decomposing a scene into its shape, reflectance and illumination is a fundamental problem in computer vision and graphics.
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.
no code implementations • EMNLP 2021 • Nikita Srivatsan, Si Wu, Jonathan T. Barron, Taylor Berg-Kirkpatrick
We propose a deep generative model that performs typography analysis and font reconstruction by learning disentangled manifolds of both font style and character shape.
2 code implementations • 24 Jun 2021 • Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-Brualla, Steven M. Seitz
A common approach to reconstruct such non-rigid scenes is through the use of a learned deformation field mapping from coordinates in each input image into a canonical template coordinate space.
1 code implementation • 3 Jun 2021 • Xiuming Zhang, Pratul P. Srinivasan, Boyang Deng, Paul Debevec, William T. Freeman, Jonathan T. Barron
This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties.
Ranked #4 on Image Relighting on Stanford-ORB
1 code implementation • 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.
4 code implementations • 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.
1 code implementation • CVPR 2021 • Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser
Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes.
1 code implementation • 10 Dec 2020 • Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Alberto Rodriguez, Phillip Isola, Tsung-Yi Lin
We then show that for complex real-world scenes from the LLFF dataset, iNeRF can improve NeRF by estimating the camera poses of novel images and using these images as additional training data for NeRF.
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.
1 code implementation • ICCV 2021 • Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, Hendrik P. A. Lensch
This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination.
Ranked #5 on Image Relighting on Stanford-ORB
3 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.
1 code implementation • ICCV 2021 • Yicheng Wu, Qiurui He, Tianfan Xue, Rahul Garg, Jiawen Chen, Ashok Veeraraghavan, Jonathan T. Barron
When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts.
2 code implementations • ICCV 2021 • Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, Ricardo Martin-Brualla
We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones.
1 code implementation • ICCV 2021 • Mahmoud Afifi, Jonathan T. Barron, Chloe LeGendre, Yun-Ta Tsai, Francois Bleibel
We present "Cross-Camera Convolutional Color Constancy" (C5), a learning-based method, trained on images from multiple cameras, that accurately estimates a scene's illuminant color from raw images captured by a new camera previously unseen during training.
no code implementations • 17 Oct 2020 • Jonathan T. Barron
We present a generalization of Schlick's bias and gain functions -- simple parametric curve-shaped functions for inputs in [0, 1].
no code implementations • 17 Oct 2020 • Tiancheng Sun, Zexiang Xu, Xiuming Zhang, Sean Fanello, Christoph Rhemann, Paul Debevec, Yun-Ta Tsai, Jonathan T. Barron, Ravi Ramamoorthi
The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces.
no code implementations • 7 Oct 2020 • Jonathan T. Barron, Jitendra Malik
A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world.
no code implementations • 1 Oct 2020 • Simon Niklaus, Xuaner Cecilia Zhang, Jonathan T. Barron, Neal Wadhwa, Rahul Garg, Feng Liu, Tianfan Xue
Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users.
1 code implementation • 9 Aug 2020 • Xiuming Zhang, Sean Fanello, Yun-Ta Tsai, Tiancheng Sun, Tianfan Xue, Rohit Pandey, Sergio Orts-Escolano, Philip Davidson, Christoph Rhemann, Paul Debevec, Jonathan T. Barron, Ravi Ramamoorthi, William T. Freeman
In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint.
1 code implementation • CVPR 2021 • Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, Daniel Duckworth
We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs.
1 code implementation • 14 Jul 2020 • Jonathan T. Barron
We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding.
13 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.
1 code implementation • 15 Jun 2020 • Orly Liba, Longqi Cai, Yun-Ta Tsai, Elad Eban, Yair Movshovitz-Attias, Yael Pritch, Huizhong Chen, Jonathan T. Barron
The sky is a major component of the appearance of a photograph, and its color and tone can strongly influence the mood of a picture.
5 code implementations • ECCV 2020 • Rico Jonschkowski, Austin Stone, Jonathan T. Barron, Ariel Gordon, Kurt Konolige, Anelia Angelova
We systematically compare and analyze a set of key components in unsupervised optical flow to identify which photometric loss, occlusion handling, and smoothness regularization is most effective.
Ranked #5 on Optical Flow Estimation on Sintel Clean unsupervised
1 code implementation • 18 May 2020 • Xuaner Cecilia Zhang, Jonathan T. Barron, Yun-Ta Tsai, Rohit Pandey, Xiuming Zhang, Ren Ng, David E. Jacobs
We propose a way to explicitly encode facial symmetry and show that our dataset and training procedure enable the model to generalize to images taken in the wild.
no code implementations • CVPR 2020 • Charles Herrmann, Richard Strong Bowen, Neal Wadhwa, Rahul Garg, Qiurui He, Jonathan T. Barron, Ramin Zabih
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance.
36 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.
Ranked #3 on Generalizable Novel View Synthesis on NERDS 360
Generalizable Novel View Synthesis Low-Dose X-Ray Ct Reconstruction +2
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.
no code implementations • CVPR 2013 • Kevin Karsch, Zicheng Liao, Jason Rock, Jonathan T. Barron, Derek Hoiem
Early work in computer vision considered a host of geometric cues for both shape reconstruction and recognition.
no code implementations • 24 Oct 2019 • Orly Liba, Kiran Murthy, Yun-Ta Tsai, Tim Brooks, Tianfan Xue, Nikhil Karnad, Qiurui He, Jonathan T. Barron, Dillon Sharlet, Ryan Geiss, Samuel W. Hasinoff, Yael Pritch, Marc Levoy
Aside from the physical limits imposed by read noise and photon shot noise, these cameras are typically handheld, have small apertures and sensors, use mass-produced analog electronics that cannot easily be cooled, and are commonly used to photograph subjects that move, like children and pets.
1 code implementation • IJCNLP 2019 • Nikita Srivatsan, Jonathan T. Barron, Dan Klein, Taylor Berg-Kirkpatrick
We propose a deep factorization model for typographic analysis that disentangles content from style.
no code implementations • 2 May 2019 • Tiancheng Sun, Jonathan T. Barron, Yun-Ta Tsai, Zexiang Xu, Xueming Yu, Graham Fyffe, Christoph Rhemann, Jay Busch, Paul Debevec, Ravi Ramamoorthi
Lighting plays a central role in conveying the essence and depth of the subject in a portrait photograph.
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.
1 code implementation • ICCV 2019 • Rahul Garg, Neal Wadhwa, Sameer Ansari, Jonathan T. Barron
Using our approach, existing monocular depth estimation techniques can be effectively applied to dual-pixel data, and much smaller models can be constructed that still infer high quality depth.
no code implementations • 5 Jan 2019 • Jian Wang, Tianfan Xue, Jonathan T. Barron, Jiawen Chen
In this work, we present a camera configuration for acquiring "stereoscopic dark flash" images: a simultaneous stereo pair in which one camera is a conventional RGB sensor, but the other camera is sensitive to near-infrared and near-ultraviolet instead of R and B.
4 code implementations • CVPR 2019 • Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, Jonathan T. Barron
Machine learning techniques work best when the data used for training resembles the data used for evaluation.
Ranked #1 on Color Image Denoising on Darmstadt Noise Dataset
no code implementations • CVPR 2019 • Tim Brooks, Jonathan T. Barron
We present a technique for synthesizing a motion blurred image from a pair of unblurred images captured in succession.
1 code implementation • 11 Jun 2018 • Neal Wadhwa, Rahul Garg, David E. Jacobs, Bryan E. Feldman, Nori Kanazawa, Robert Carroll, Yair Movshovitz-Attias, Jonathan T. Barron, Yael Pritch, Marc Levoy
Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background.
3 code implementations • CVPR 2018 • Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, Robert Carroll
We present a technique for jointly denoising bursts of images taken from a handheld camera.
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.
2 code implementations • 10 Jul 2017 • Michaël Gharbi, Jiawen Chen, Jonathan T. Barron, Samuel W. Hasinoff, Frédo Durand
For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms.
1 code implementation • 24 Apr 2017 • Jonathan T. Barron
Exponential Linear Units (ELUs) are a useful rectifier for constructing deep learning architectures, as they may speed up and otherwise improve learning by virtue of not have vanishing gradients and by having mean activations near zero.
3 code implementations • CVPR 2019 • Jonathan T. Barron
We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions.
2 code implementations • CVPR 2017 • Jonathan T. Barron, Yun-Ta Tsai
We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm which solves illuminant estimation by reducing it to a spatial localization task on a torus.
no code implementations • CVPR 2016 • Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, Alan L. Yuille
Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems.
2 code implementations • 10 Nov 2015 • Jonathan T. Barron, Ben Poole
We present the bilateral solver, a novel algorithm for edge-aware smoothing that combines the flexibility and speed of simple filtering approaches with the accuracy of domain-specific optimization algorithms.
1 code implementation • ICCV 2015 • Jonathan T. Barron
Color constancy is the problem of inferring the color of the light that illuminated a scene, usually so that the illumination color can be removed.
no code implementations • CVPR 2015 • Jonathan T. Barron, Andrew Adams, YiChang Shih, Carlos Hernandez
Given a stereo pair it is possible to recover a depth map and use that depth to render a synthetically defocused image.
1 code implementation • 3 Mar 2015 • Jordi Pont-Tuset, Pablo Arbelaez, Jonathan T. Barron, Ferran Marques, Jitendra Malik
We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG).
no code implementations • CVPR 2014 • Pablo Arbelaez, Jordi Pont-Tuset, Jonathan T. Barron, Ferran Marques, Jitendra Malik
We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG).
no code implementations • CVPR 2013 • Jonathan T. Barron, Jitendra Malik
Our model takes as input a single RGB-D image and produces as output an improved depth map, a set of surface normals, a reflectance image, a shading image, and a spatially varying model of illumination.