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- I'm interested in computer vision, machine-learning, and computational photography. Most of my research is about figuring out the physical world (shape, paint, light, etc) that created a single image. I also work in biomedical image analysis and astronomy.
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- Color Constancy, Intrinsic Images, and Shape Estimation Jonathan T. Barron , Jitendra Malik European Conference on Computer Vision (ECCV) , 2012 supplementary material /
- bibtex /
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- We present SIRFS (shape, illumination, and reflectance from shading), the first unified model for recovering shape, chromatic illumination, and reflectance from a single image.
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- Shape, Albedo, and Illumination from a Single Image of an Unknown Object Jonathan T. Barron , Jitendra Malik Computer Vision and Pattern Recognition (CVPR) , 2012 supplementary material / bibtex / poster
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- We present SAIFS (shape, albedo, and illumination from shading), which produces reasonable results on arbitrary grayscale images of masked objects, and outperforms all previous grayscale "intrinsic images" algorithms.
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- A Category-Level 3-D Object Dataset: Putting the Kinect to Work Allison Janoch , Sergey Karayev , Yangqing Jia , Jonathan T. Barron , Mario Fritz , Kate Saenko , Trevor Darrell International Conference on Computer Vision (ICCV) 3DRR Workshop , 2011 bibtex
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- Using the Microsoft Kinect, we gather a large dataset of indoor crowded scenes. We investigate ways to unify state-of-the-art object detection systems and improve them with depth information.
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- High-Frequency Shape and Albedo from Shading using Natural Image Statistics Jonathan T. Barron , Jitendra Malik Computer Vision and Pattern Recognition (CVPR) , 2011 bibtex
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- To solve shape-from-shading and intrinsic images simultaneously, we impose "naturalness" priors over albedo and shape and recover the most likely albedo and shape that together explain a single image.
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- Discovering Efficiency in Coarse-To-Fine Texture Classification Jonathan T. Barron , Jitendra Malik Technical Report , 2010 bibtex
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- We introduce a model and feature representation for joint texture classification and segmentation that learns how to classify accurately and when to classify efficiently. This allows for sub-linear coarse-to-fine classification.
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- Blind Date: Using proper motions to determine the ages of historical images Jonathan T. Barron , David W. Hogg , Dustin Lang , Sam Roweis The Astronomical Journal , 136, 2008
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- Using only raw pixel data and known catalog proper motions, it is possible to accurately estimate the date of origin of historical imagery. This allows us to retrieve lost meta-data, improve astrometric calibration, and re-estimate proper motions.
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Parallelizing Reinforcement Learning
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Jonathan T. Barron ,
Dave Golland ,
Nicholas J. Hay , 2009
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- Jon Barron
- Soham Shinde
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- Jon Barron
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- I am a senior research scientist at Google Research , where I work on computer vision and computational photography. At Google I've worked on Lens Blur , HDR+ , Jump , and Glass .
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- I did my PhD at UC Berkeley , where I was advised by Jitendra Malik and funded by the NSF GRFP . I've spent time at Google[x] , MIT CSAIL , Captricity , NASA Ames , Google NYC , the NYU MRL , Novartis , and Astrometry.net . I did my bachelors at the University of Toronto .
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- Email /
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- LinkedIn
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- Research
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- I'm interested in computer vision, machine learning, statistics, optimization, image processing, virtual reality, and computational photography. Much of my research is about inferring the physical world (shape, depth, motion, paint, light, colors, etc) from images. I have also worked in astronomy and biology. Papers I especially like are highlighted .
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- A Hardware-Friendly Bilateral Solver for Real-Time Virtual Reality Video Amrita Mazumdar , Armin Alaghi , Jonathan T. Barron , David Gallup , Luis Ceze , Mark Oskin , Steven M. Seitz High-Performance Graphics (HPG) , 2017 project page
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- A reformulation of the bilateral solver can be implemented efficiently on GPUs and FPGAs.
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- Deep Bilateral Learning for Real-Time Image Enhancement Michaël Gharbi , Jiawen Chen , Jonathan T. Barron , Samuel W. Hasinoff , Frédo Durand SIGGRAPH , 2017 project page
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- By training a deep network in bilateral space we can learn a model for high-resolution and real-time image enhancement.
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- A More General Robust Loss Function Jonathan T. Barron arXiv Preprint , 2017
- A single robust loss function is a superset of many other common robust loss functions.
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- Fast Fourier Color Constancy Jonathan T. Barron , Yun-Ta TsaiComputer Vision and Pattern Recognition (CVPR) , 2017 supplement
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- Color space can be aliased, allowing white balance models to be learned and evaluated in the frequency domain. This improves accuracy by 13-20% and speed by 250-3000x.
- This technology is used by Google Photos .
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- Jump: Virtual Reality Video Robert Anderson , David Gallup , Jonathan T. Barron , Janne Kontkanen , Noah Snavely , Carlos Hernández , Sameer Agarwal , Steven M Seitz SIGGRAPH Asia , 2016supplement
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- Using computer vision and a ring of cameras, we can make video for virtual reality headsets that is both stereo and 360°.
- This technology is used by Jump .
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- Burst Photography for High Dynamic Range and Low-Light Imaging on Mobile Cameras Samuel W. Hasinoff , Dillon Sharlet, Ryan Geiss , Andrew Adams , Jonathan T. Barron , Florian Kainz, Jiawen Chen , Marc Levoy SIGGRAPH Asia , 2016project page
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- Mobile phones can take beautiful photographs in low-light or high dynamic range environments by aligning and merging a burst of images.
- This technology is used by the Nexus HDR+ feature.
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- The Fast Bilateral Solver Jonathan T. Barron , Ben Poole European Conference on Computer Vision (ECCV) , 2016 (Best Paper Honorable Mention) arXiv
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- video (they messed up my slides, use →)
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- depth super-res results
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- Our solver smooths things better than other filters and faster and than other optimization algorithms, and you can backprop through it.
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- Geometric Calibration for Mobile, Stereo, Autofocus Cameras Stephen DiVerdi ,
- Jonathan T. Barron Winter Conference on Applications of Computer Vision (WACV) , 2016 bibtex
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- Standard techniques for stereo calibration don't work for cheap mobile cameras.
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- Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform Computer Vision and Pattern Recognition (CVPR) , 2016 Liang-Chieh Chen , Jonathan T. Barron , George Papandreou , Kevin Murphy , Alan L. Yuille bibtex /
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- By integrating an edge-aware filter into a convolutional neural network we can learn an edge-detector while improving semantic segmentation.
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- Convolutional Color Constancy Jonathan T. Barron International Conference on Computer Vision (ICCV) , 2015 supplement / bibtex / video (or mp4 )
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- By framing white balance as a chroma localization task we can discriminatively learn a color constancy model that beats the state-of-the-art by 40%.
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- Scene Intrinsics and Depth from a Single Image
- Evan Shelhamer , Jonathan T. Barron , Trevor Darrell International Conference on Computer Vision (ICCV) Workshop , 2015 bibtex
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- The monocular depth estimates produced by fully convolutional networks can be used to inform intrinsic image estimation.
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- Fast Bilateral-Space Stereo for Synthetic Defocus Jonathan T. Barron , Andrew Adams , YiChang Shih, Carlos Hernández Computer Vision and Pattern Recognition (CVPR) , 2015 (Oral Presentation) abstract / supplement / bibtex /
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- By embedding a stereo optimization problem in "bilateral-space" we can very quickly solve for an edge-aware depth map, letting us render beautiful depth-of-field effects.
- This technology is used by the Google Camera "Lens Blur" feature.
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+ I am a Machine Learning Engineer at Clutterbot , working on applied ML for robotics. I graduated with a B.E. in Electronics and Communications Engineering with a minor in Data Science from
+ BITS Pilani, K.K Birla Goa Campus
+ in July 2025. I am interested in interdisciplinary research, particularly in the domains of Computer Vision and Applied Machine Learning.
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+ Previously, I was a Research Associate at Nanyang Technological University , working on Gaze Estimation and other computer vision techniques with Dr. Yuvaraj and Dr. Amalin . My work involved developing advanced gaze estimation models using deep learning and analyzing correlations between gaze patterns and student engagement to assess attention in educational settings.
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+ Additionally, I have experience in 3D Computer Vision, specifically in Point Cloud Processing, where I worked on implementing supervised algorithms for Human Cloth Segmentation - CloSe .
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+ In conclusion, my research revolves around Computer Vision , 3D Modeling , Applied Machine Learning , HCI , and Robotics
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+ During my undergraduate years, I contributed to various student-led projects at BITS Pilani, Goa, including
+ Project Kratos ,
+ where I was involved in the Autonomous Subsystem of a Mars Rover prototype. I also served as the Events and Initiatives Head at the
+ Center for Technical Education, BITS Goa .
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+ Outside of work, I'm usually found brewing Blue Tokai in a French press, bouldering, or planning for a trek to the Himalayas! =)
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+ Email
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+ CV
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+ GitHub
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+ LinkedIn
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+ Machine Learning Engineer | Clutterbot
+ Built an end-to-end pipeline for training and deploying segmentation and detection models for indoor navigation, packaging deployment deliverables for the Autonomous Navigation team. Migrated a complex perception system to the Qualcomm DragonWing QCS6490P chipset and NVIDIA IoT kits, improving edge inference speed by ~30% in barebones Linux environments using GStreamer. Also designed and deployed a custom 6DOF pose estimation proof-of-concept pipeline to ensure reliable target locking and rebasing during the hardware migration.
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+ Research Associate | Nanyang Technological University
+ Developed and benchmarked a Vision Transformer (ViViT) for classroom activity recognition on a 927-clip EduNet subset, achieving 88% test accuracy by fine-tuning a Kinetics-400 pre-trained model with a 224x224 video preprocessing pipeline. Validated generalizability on an independent 100-video dataset (72% accuracy) and generated gradient-based saliency maps to confirm focus on key regions (e.g. raised hand, board writing). Analyzed gaze and activity patterns to derive quantitative student engagement measures.
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+ Intern | TCS Research
+ Implemented and optimized a PyQt5-based GUI integrating SAM for image segmentation, enabling efficient AI-assisted annotation. Developed and automated a mechanism to aggregate individually segmented regions into larger sub-scenes using advanced image processing techniques, advancing the semantic understanding of complex images and reducing manual effort.
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+ Research Intern | CSIR-CEERI, Pilani
+ Worked under the supervision of Dr. Dhiraj Sangwan on restoring and segmenting deteriorated Rajasthani murals using deep learning models like U-Net++, DeepLabV3+, PSPNet, and FPN. Developed a synthetic damaged-image generation pipeline using GANs (StyleGAN2-ADA) and crafted diverse binary masks to simulate complex real-world mural damage. Implemented a state-of-the-art inpainting pipeline achieving an SSIM score of 0.9812 for reinstating missing sections.
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+ Subscene Segmentation for Artwork Scene Understanding Paper
+ Soham Shinde and Vikram Jamwal
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+ Submitted at ECCV AI4VA Workshop .
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+ Damage Segmentation and Restoration of Ancient Wall Paintings for Preserving
+ Cultural Heritage Paper
+ HS Baath, S. Shinde , J. Keniya, PR. Mishra, A. Saini and D. Sangwan
+ Accepted at the 8th International Conference on Computer Vision & Image Processing (CVIP-2023)
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+ CloSe++ Feb '24 - May '24
+ Supervised by Dr. Garvita Tiwari
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+ Extended the CloSe -Net framework for fine-grained 3D clothing segmentation from coloured point clouds. Refined edge detection mechanisms to produce sharper boundaries between clothing types, addressing the blurriness in the original model, and automated clothing-type detection to remove manual input and improve usability.
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+ Project Visio Oct '22 - Aug '23
+ Supervised by Prof. Sougata Sen
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+ Engineered smart-glasses to aid the visually impaired, integrating computer vision techniques for object detection and scene understanding. Developed Tiny-ML models for efficient inference on ESP microcontrollers and designed a companion Android app to interface with the smart-glass system, ensuring seamless real-time assistance over WiFi.
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+ Project Kratos Sept '22 - Jan '23
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+ Contributed to the Autonomous Subsystem of BITS Goa's student-managed Mars Rover. Optimized autonomous navigation in ROS, composed and modified path-planning algorithms (A*, Dijkstra, SLAM) in the Gazebo simulator interfaced with an Nvidia Jetson Xavier, and improved navigation through OpenCV-based object detection.
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- Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
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- Jordi Pont-Tuset , Pablo Arbeláez , Jonathan T. Barron , Ferran Marqués , Jitendra Malik
-IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2017project page /
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- fast eigenvector code
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- We produce state-of-the-art contours, regions and object candidates, and we compute normalized-cuts eigenvectors 20× faster.
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- This paper subsumes our CVPR 2014 paper.
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+ This template is a modification to Jon Barron's website. Find the source code to my website here .
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- Shape, Illumination, and Reflectance from Shading
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- Jonathan T. Barron , Jitendra Malik IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2015supplement / bibtex / keynote (or powerpoint , PDF ) / video / code & data / rant / kudos
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- We present SIRFS , which can estimate shape, chromatic illumination, reflectance, and shading from a single image of an masked object.
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- This paper subsumes our CVPR 2011, CVPR 2012, and ECCV 2012 papers.
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- Multiscale Combinatorial Grouping
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- Pablo Arbeláez , Jordi Pont-Tuset , Jonathan T. Barron , Ferran Marqués , Jitendra Malik
- Computer Vision and Pattern Recognition (CVPR) , 2014 project page /
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- This paper is subsumed by our journal paper .
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- Volumetric Semantic Segmentation using Pyramid Context Features
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- Jonathan T. Barron , Pablo Arbeláez , Soile V. E. Keränen , Mark D. Biggin , David W. Knowles , Jitendra Malik
- International Conference on Computer Vision (ICCV) , 2013 supplement /
- poster /
- bibtex / video 1 (or mp4 ) / video 2 (or mp4 ) / code & data
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- We present a technique for efficient per-voxel linear classification, which enables accurate and fast semantic segmentation of volumetric Drosophila imagery.
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- 3D Self-Portraits
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- Hao Li , Etienne Vouga , Anton Gudym, Linjie Luo , Jonathan T. Barron , Gleb Gusev
- SIGGRAPH Asia , 2013 video / shapify.me / bibtex
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- Our system allows users to create textured 3D models of themselves in arbitrary poses using only a single 3D sensor.
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- Intrinsic Scene Properties from a Single RGB-D Image
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- Jonathan T. Barron , Jitendra Malik Computer Vision and Pattern Recognition (CVPR) , 2013 (Oral Presentation) supplement / bibtex / talk / keynote (or powerpoint , PDF ) / code & data
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- By embedding mixtures of shapes & lights into a soft segmentation of an image, and by leveraging the output of the Kinect, we can extend SIRFS to scenes.
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- Boundary Cues for 3D Object Shape Recovery
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- Kevin Karsch ,
- Zicheng Liao ,
- Jason Rock ,
- Jonathan T. Barron ,
- Derek Hoiem
- Computer Vision and Pattern Recognition (CVPR) , 2013 supplement / bibtex
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- Boundary cues (like occlusions and folds) can be used for shape reconstruction, which improves object recognition for humans and computers.
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- Color Constancy, Intrinsic Images, and Shape Estimation
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- Jonathan T. Barron , Jitendra Malik European Conference on Computer Vision (ECCV) , 2012supplement / bibtex / poster / video
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- This paper is subsumed by SIRFS .
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- Shape, Albedo, and Illumination from a Single Image of an Unknown Object
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- Jonathan T. Barron , Jitendra Malik Computer Vision and Pattern Recognition (CVPR) , 2012supplement / bibtex / poster
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- This paper is subsumed by SIRFS .
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- A Category-Level 3-D Object Dataset: Putting the Kinect to Work
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- Allison Janoch , Sergey Karayev , Yangqing Jia , Jonathan T. Barron , Mario Fritz , Kate Saenko , Trevor Darrell International Conference on Computer Vision (ICCV) 3DRR Workshop , 2011bibtex / "smoothing" code
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- We present a large RGB-D dataset of indoor scenes and investigate ways to improve object detection using depth information.
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- High-Frequency Shape and Albedo from Shading using Natural Image Statistics
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- Jonathan T. Barron , Jitendra Malik Computer Vision and Pattern Recognition (CVPR) , 2011bibtex
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- This paper is subsumed by SIRFS .
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- Discovering Efficiency in Coarse-To-Fine Texture Classification
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- Jonathan T. Barron , Jitendra Malik Technical Report , 2010bibtex
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- We introduce a model and feature representation for joint texture classification and segmentation that learns how to classify accurately and when to classify efficiently. This allows for sub-linear coarse-to-fine classification.
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- Blind Date: Using Proper Motions to Determine the Ages of Historical Images
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- Jonathan T. Barron , David W. Hogg , Dustin Lang , Sam Roweis The Astronomical Journal , 136, 2008
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- Using the relative motions of stars we can accurately estimate the date of origin of historical astronomical images.
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- Cleaning the USNO-B Catalog Through Automatic Detection of Optical Artifacts
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- Jonathan T. Barron , Christopher Stumm , David W. Hogg , Dustin Lang , Sam Roweis The Astronomical Journal , 135, 2008
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- We use computer vision techniques to identify and remove diffraction spikes and reflection halos in the USNO-B Catalog.
- In use at Astrometry.net
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+ font-weight: 400;
+ src: local('Lato Regular'), local('Lato-Regular'), url(https://fonts.gstatic.com/s/lato/v15/S6uyw4BMUTPHjx4wXiWtFCc.woff2) format('woff2');
+ unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* latin-ext */
+@font-face {
+ font-family: 'Lato';
+ font-style: normal;
+ font-weight: 700;
+ src: local('Lato Bold'), local('Lato-Bold'), url(https://fonts.gstatic.com/s/lato/v15/S6u9w4BMUTPHh6UVSwaPGQ3q5d0N7w.woff2) format('woff2');
+ unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+ font-family: 'Lato';
+ font-style: normal;
+ font-weight: 700;
+ src: local('Lato Bold'), local('Lato-Bold'), url(https://fonts.gstatic.com/s/lato/v15/S6u9w4BMUTPHh6UVSwiPGQ3q5d0.woff2) format('woff2');
+ unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+
+a {
+ color: #1772d0;
+ text-decoration: none;
+}
+
+a:focus,
+a:hover {
+ color: #f09228;
+ text-decoration: none;
+}
+
+body,
+td,
+th,
+tr,
+p,
+a {
+ font-family: 'Lato', Verdana, Helvetica, sans-serif;
+ font-size: 14px
+}
+
+strong {
+ font-family: 'Lato', Verdana, Helvetica, sans-serif;
+ font-size: 14px;
+}
+
+heading {
+ font-family: 'Lato', Verdana, Helvetica, sans-serif;
+ font-size: 22px;
+}
+
+papertitle {
+ font-family: 'Lato', Verdana, Helvetica, sans-serif;
+ font-size: 14px;
+ font-weight: 700
+}
+
+name {
+ font-family: 'Lato', Verdana, Helvetica, sans-serif;
+ font-size: 32px;
+}
+
+.one {
+ width: 160px;
+ height: 160px;
+ position: relative;
+}
+
+.two {
+ width: 160px;
+ height: 160px;
+ position: absolute;
+ transition: opacity .2s ease-in-out;
+ -moz-transition: opacity .2s ease-in-out;
+ -webkit-transition: opacity .2s ease-in-out;
+}
+
+.fade {
+ transition: opacity .2s ease-in-out;
+ -moz-transition: opacity .2s ease-in-out;
+ -webkit-transition: opacity .2s ease-in-out;
+}
+
+span.highlight {
+ background-color: #ffffd0;
+}
+
+.CodeMirror {
+ font-size: .8em;
+ height: auto;
+}
+
+.CodeMirror-scroll {
+ overflow-y: hidden;
+ overflow-x: auto;
+}
+
+#header_img {
+ margin-top: 2em;
+ margin-bottom: 1em;
+}
+
+.list-inline {
+ list-style: none;
+ margin-left: -0.5em;
+ margin-right: -0.5em;
+ padding-left: 0;
+}
+
+.list-inline > li {
+ display: inline-block;
+ margin-left: 0.5em;
+ margin-right: 0.5em;
+}
\ No newline at end of file
diff --git a/mipnerf/css/bootstrap.min.css b/mipnerf/css/bootstrap.min.css
new file mode 100644
index 0000000000..2df367cb5e
--- /dev/null
+++ b/mipnerf/css/bootstrap.min.css
@@ -0,0 +1,11 @@
+@import url("https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,400,700");/*!
+ * bootswatch v3.3.5
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2015 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.3.5 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ *//*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */html{font-family:sans-serif;-ms-text-size-adjust:100%;-webkit-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:bold}dfn{font-style:italic}h1{font-size:2em;margin:0.67em 0}mark{background:#ff0;color:#000}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sup{top:-0.5em}sub{bottom:-0.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;height:0}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace, monospace;font-size:1em}button,input,optgroup,select,textarea{color:inherit;font:inherit;margin:0}button{overflow:visible}button,select{text-transform:none}button,html input[type="button"],input[type="reset"],input[type="submit"]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{border:0;padding:0}input{line-height:normal}input[type="checkbox"],input[type="radio"]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type="number"]::-webkit-inner-spin-button,input[type="number"]::-webkit-outer-spin-button{height:auto}input[type="search"]{-webkit-appearance:textfield;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}input[type="search"]::-webkit-search-cancel-button,input[type="search"]::-webkit-search-decoration{-webkit-appearance:none}fieldset{border:1px solid #c0c0c0;margin:0 2px;padding:0.35em 0.625em 0.75em}legend{border:0;padding:0}textarea{overflow:auto}optgroup{font-weight:bold}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}/*! 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+ mip-NeRF
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+ Mip-NeRF : A Multiscale Representation
+ ICCV 2021 (Oral, Best Paper Honorable Mention)
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+ Abstract
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+The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or aliased when training or testing images observe scene content at different resolutions. The straightforward solution of supersampling by rendering with multiple rays per pixel is impractical for NeRF, because rendering each ray requires querying a multilayer perceptron hundreds of times. Our solution, which we call "mip-NeRF" (à la "mipmap"), extends NeRF to represent the scene at a continuously-valued scale.
+By efficiently rendering anti-aliased conical frustums instead of rays, mip-NeRF reduces objectionable aliasing artifacts and significantly improves NeRF's ability to represent fine details, while also being 7% faster than NeRF and half the size.
+Compared to NeRF, mip-NeRF reduces average error rates by 17% on the dataset presented with NeRF and by 60% on a challenging multiscale variant of that dataset that we present. mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.
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+ Integrated Positional Encoding
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+ Typical positional encoding (as used in Transformer networks and Neural Radiance Fields) maps a single point in space to a feature vector, where each element is generated by a sinusoid with an exponentially increasing frequency:
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+ Here, we show how these feature vectors change as a function of a point moving in 1D space.
+ integrated positional encoding considers Gaussian regions of space, rather than infinitesimal points. This provides a natural way to input a "region" of space as query to a coordinate-based neural network, allowing the network to reason about sampling and aliasing. The expected value of each positional encoding component has a simple closed form:
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+ We can see that when considering a wider region, the higher frequency features automatically shrink toward zero, providing the network with lower-frequency inputs. As the region narrows, these features converge to the original positional encoding.
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+ Mip-NeRF
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+ We use integrated positional encoding to train NeRF to generate anti-aliased renderings. Rather than casting an infinitesimal ray through each pixel, we instead cast a full 3D cone . For each queried point along a ray, we consider its associated 3D conical frustum. Two different cameras viewing the same point in space may result in vastly different conical frustums, as illustrated here in 2D:
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+ In order to pass this information through the NeRF network, we fit a multivariate Gaussian to the conical frustum and use the integrated positional encoding described above to create the input feature vector to the network.
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+ Results
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+ We train NeRF and mip-NeRF on a dataset with images at four different resolutions. Normal NeRF (left) is not capable of learning to represent the same scene at multiple levels of detail, with blurring in close-up shots and aliasing in low resolution views, while mip-NeRF (right) both preserves sharp details in close-ups and correctly renders the zoomed-out images.
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+ We can also manipulate the integrated positional encoding by using a larger or smaller radius than the true pixel footprint, exposing the continuous level of detail learned within a single network:
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+ Related links
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+ Wikipedia provides an excellent introduction to spatial anti-aliasing techniques.
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+ Mipmaps were introduced by Lance Williams in his paper "Pyramidal Parametrics" (Williams (1983) ).
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+ Amanatides (1984) first proposed the idea of replacing rays with cones in computer graphics rendering.
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+ The closely related concept of ray differentials (Igehy (1999) ) is used in most modern renderers to antialias textures and other material buffers during ray tracing.
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+ Cone tracing has been used along with prefiltered voxel-based representations of scene geometry for speeding up indirect illumination calculations in Crassin et al. (2011) .
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+ Mip-NeRF was implemented on top of the JAXNeRF codebase.
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+ Acknowledgements
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+ We thank Janne Kontkanen and David Salesin for their comments on the text, Paul Debevec for constructive discussions, and Boyang Deng for JaxNeRF.
+ Michaël Gharbi .
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mip-NeRF 360
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+ Mip-NeRF 360 : Unbounded
+ CVPR 2022 (Oral Presentation)
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+ Rendered images and depths from our model.
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+ Abstract
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+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. In this setting, existing NeRF-like models often produce blurry or low-resolution renderings (due to the unbalanced detail and scale of nearby and distant objects), are slow to train, and may exhibit artifacts due to the inherent ambiguity of the task of reconstructing a large scene from a small set of images. We present an extension of mip-NeRF (a NeRF variant that addresses sampling and aliasing) that uses a non-linear scene parameterization, online distillation, and a novel distortion-based regularizer to overcome the challenges presented by unbounded scenes. Our model, which we dub "mip-NeRF 360" as we target scenes in which the camera rotates 360 degrees around a point, reduces mean-squared error by 54% compared to mip-NeRF, and is able to produce realistic synthesized views and detailed depth maps for highly intricate, unbounded real-world scenes.
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+ Acknowledgements
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+ Thanks to Ricardo Martin-Brualla and David Salesin for their comments on the text, and to George Drettakis and Georgios Kopanas for graciously assisting us with our baseline evaluation.
+ Michaël Gharbi .
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Zip-NeRF
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+ Zip-NeRF : Anti-Aliased Grid-Based Neural Radiance Fields
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+ ICCV 2023 (Oral Presentation, Best Paper Finalist)
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+ Abstract
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+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. However, these grid-based approaches lack an explicit understanding of scale and therefore often introduce aliasing, usually in the form of jaggies or missing scene content. Anti-aliasing has previously been addressed by mip-NeRF 360, which reasons about sub-volumes along a cone rather than points along a ray, but this approach is not natively compatible with current grid-based techniques. We show how ideas from rendering and signal processing can be used to construct a technique that combines mip-NeRF 360 and grid-based models such as Instant NGP to yield error rates that are 8%-77% lower than either prior technique, and that trains 24x faster than mip-NeRF 360.
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+ 360° Video Flythroughs
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+ Multisampling
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+ When Training
+ When Rendering
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+ We use multisampling to approximate the average NGP feature over a conical frustum, by constructing a 6-sample pattern that exactly matches the frustum's first and second moments. When training, we randomly rotate and flip (along the ray axis) each pattern, and when rendering we deterministically flip and rotate each adjacent pattern by 30 degrees.
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+ XY aliasing
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+ A naive baseline (left) combining mip-NeRF 360 and Instant NGP results in aliasing as the camera moves laterally. Our full method (right) produces prefiltered renderings that do not flicker or shimmer.
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+ Z aliasing
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+ The proposal network used for resampling points along rays in mip-NeRF 360 results in an artifact we refer to as z-aliasing , where foreground content alternately appears and disappears as the camera moves toward or away from scene content. Z-aliasing occurs when the initial set of samples from the proposal network is not dense enough and misses thin structures, such as the chair above. Missed content can not be recovered by later rounds of sampling, since no future samples will be placed at that location along the ray. Our improvements to proposal network supervision result in a prefiltered proposal output that preserves the foreground object for all frames in this sequence. The plots above depict samples along a ray for three rounds of resampling (blue, orange, and green lines), with the y axis showing rendering weight (how much each interval contributes to the final rendered color), as a normalized probability density.
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+ Acknowledgements
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+ Thanks to Janne Kontkanen, Rick Szeliski, and David Salesin for their comments on the text, and to Ricardo Martin-Brualla, Keunhong Park, Ben Poole, Aleksander Hołyński, Etienne Pot, Kostas Rematas, Daniel Duckworth, Marcos Seefelder, Cardin Moffett, and Peter Zhizhin for their advice and help.
+ Michaël Gharbi and Ref-NeRF .
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