Pratul Srinivasan (original) (raw)

Research and Publications

* denotes equal contribution co-authorship

Your browser does not support the video tag.	IllumiNeRF: 3D Relighting without Inverse Rendering Xiaoming Zhao,Pratul Srinivasan,Dor Verbin,Keunhong Park,Ricardo Martin Brualla, Philipp Henzler arXiv, 2024 project page /arXiv 3D relighting by distilling samples from a 2D image relighting diffusion model into a latent-variable NeRF.
Your browser does not support the video tag.	Nuvo: Neural UV Mapping for Unruly 3D Representations Pratul Srinivasan,Stephan J. Garbin,Dor Verbin,Jonathan T. Barron,Ben Mildenhall ECCV, 2024 project page /video /arXiv Use neural fields to recover editable UV mappings for challenging geometry (e.g. NeRFs, marching cubes meshes, DreamFusion).
Your browser does not support the video tag.	CAT3D: Create Anything in 3D with Multi-View Diffusion Models Ruiqi Gao*,Aleksander Holynski*, Philipp Henzler,Arthur Brussee, Ricardo Martin Brualla, Pratul Srinivasan,Jonathan T. Barron,Ben Poole* arXiv, 2024 project page /arXiv A system built around diffusion and NeRF that does text-to-3D, image-to-3D, and few-view reconstruction, trains in 1 minute, and renders at 60FPS in a browser.
Your browser does not support the video tag.	Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis Christian Reiser,Stephan J. Garbin,Pratul Srinivasan,Dor Verbin,Richard Szeliski,Ben Mildenhall,Jonathan T. Barron,Peter Hedman*,Andreas Geiger* SIGGRAPH, 2024 project page /video /arXiv Applying anti-aliasing to a discrete opacity grid lets you render a hard representation into a soft image, and this enables highly-detailed mesh recovery.
Your browser does not support the video tag.	Eclipse: Disambiguating Illumination and Materials using Unintended Shadows Dor Verbin,Ben Mildenhall,Peter Hedman,Jonathan T. Barron,Todd Zickler,Pratul Srinivasan CVPR, 2024 (Oral Presentation) project page /video /arXiv Shadows cast by unobserved occluders provide a high-frequency cue for recovering illumination and materials.
Your browser does not support the video tag.	ReconFusion: 3D Reconstruction with Diffusion Priors Rundi Wu*,Ben Mildenhall*,Philipp Henzler,Keunhong Park,Ruiqi Gao,Daniel Watson,Pratul Srinivasan,Dor Verbin,Jonathan T. Barron,Ben Poole,Aleksander Holynski* CVPR, 2024 project page /arXiv Using a multi-image diffusion model as a regularizer lets you recover high-quality radiance fields from just a handful of images.
Your browser does not support the video tag.	Generative Powers of Ten Xiaojuan Wang,Janne Kontkanen,Brian Curless,Steve Seitz,Ira Kemelmacher,Ben Mildenhall,Pratul Srinivasan,Dor Verbin,Aleksander Holynski CVPR, 2024 project page /arXiv Use a text-to-image model to generate consistent content across drastically varying scales.
Your browser does not support the video tag.	Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields Jonathan T. Barron,Ben Mildenhall,Dor Verbin,Pratul Srinivasan,Peter Hedman ICCV, 2023 (Oral Presentation, Best Paper Finalist) project page /video /arXiv Combining mip-NeRF 360 and Instant NGP lets us reconstruct huge scenes.
Your browser does not support the video tag.	BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis Lior Yariv*,Peter Hedman*,Christian Reiser,Dor Verbin,Pratul Srinivasan,Richard Szeliski,Jonathan T. Barron,Ben Mildenhall SIGGRAPH, 2023 project page /video /arXiv We use SDFs to bake a NeRF-like model into a high quality mesh and do real-time view synthesis.
Your browser does not support the video tag.	MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes Christian Reiser,Richard Szeliski,Dor Verbin,Pratul Srinivasan,Ben Mildenhall,Andreas Geiger,Jonathan T. Barron,Peter Hedman SIGGRAPH, 2023 project page /video /arXiv We use volumetric rendering with a sparse 3D feature grid and 2D feature planes to do real-time view synthesis.
	VQ3D: Learning a 3D Generative Model on ImageNet Kyle Sargent,Jing Yu Koh,Han Zhang,Huiwen Chang,Charles Herrmann,Pratul Srinivasan,Jiajun Wu,Deqing Sun CVPR, 2023 (Oral Presentation, Best Paper Finalist) project page /arXiv /ViT-VQGAN plus a NeRF-based decoder that enables both single-image view synthesis and 3D generation.
Your browser does not support the video tag.	PersonNeRF: Personalized Reconstruction from Photo Collections Chung-Yi Weng,Pratul Srinivasan,Brian Curless,Ira Kemelmacher-Shlizerman CVPR, 2023 project page /arXiv /video Construct a personalized 3D model from an unstructed photo collection.
Your browser does not support the video tag.	Gravitationally Lensed Black Hole Emission Tomography Aviad Levis*,Pratul Srinivasan*,Andrew A. Chael,Ren Ng,Katherine L. Bouman CVPR, 2022 project page /arXiv /video We apply ideas from NeRF to the problem of reconstructing the dynamic emissive volume around a black hole.
Your browser does not support the video tag.	Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields Dor Verbin,Peter Hedman,Ben Mildenhall,Todd Zickler,Jonathan T. Barron,Pratul Srinivasan CVPR, 2022 (Oral Presentation, Best Student Paper Honorable Mention) project page /arXiv /video We fix NeRF's shortcomings when representing shiny materials, greatly improve NeRF's normal vectors, and enable intuitive material editing.
Your browser does not support the video tag.	Block-NeRF: Scalable Large Scene Neural View Synthesis Matthew Tancik,Vincent Casser,Xinchen Yan,Sabeek Pradhan,Ben Mildenhall,Pratul Srinivasan,Jonathan T. Barron,Henrik Kretzschmar CVPR, 2022 (Oral Presentation) project page /arXiv /video We build city-scale scenes from many NeRFs, trained using millions of images.
Your browser does not support the video tag.	HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video Chung-Yi Weng,Brian Curless,Pratul Srinivasan,Jonathan T. Barron,Ira Kemelmacher-Shlizerman CVPR, 2022 (Oral Presentation) project page /arXiv /video Free-viewpoint rendering of any body pose from a monocular video of a human.
	NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images Ben Mildenhall,Peter Hedman,Ricardo Martin-Brualla,Pratul Srinivasan,Jonathan T. Barron CVPR, 2022 (Oral Presentation) project page /arXiv /video We train NeRFs directly on linear raw camera images, enabling new HDR view synthesis applications and greatly increasing robustness to camera noise.
Your browser does not support the video tag.	Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields Jonathan T. Barron,Ben Mildenhall,Dor Verbin,Pratul Srinivasan,Peter Hedman CVPR, 2022 (Oral Presentation) project page /arXiv /video We extend mip-NeRF to produce photorealistic results on unbounded scenes.
Your browser does not support the video tag.	Urban Radiance Fields Konstantinos Rematas,Andrew Liu,Pratul Srinivasan,Jonathan T. Barron,Andrea Tagliasacchi,Tom Funkhouser, Vittorio Ferrari CVPR, 2022 project page /arXiv /video We incorporate lidar data and explicitly model the sky to reconstruct urban environments with NeRF.
	Dense Depth Priors for Neural Radiance Fields from Sparse Input Views Barbara Roessle,Jonathan T. Barron,Ben Mildenhall,Pratul Srinivasan,Matthias Nießner CVPR, 2022 project page /arXiv /video We apply dense depth completion techniques to freely-available sparse stereo data to guide NeRF reconstructions from few input images.
	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 ACM Transactions on Graphics (SIGGRAPH Asia), 2021 project page /video /arXiv We recover relightable NeRF-like models from images under a single unknown lighting condition.
Your browser does not support the video tag.	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, Best Paper Honorable Mention) project page /arXiv /video 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.
Your browser does not support the video tag.	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 We "bake" a trained NeRF into a sparse voxel grid of colors and features in order to render it in real-time.
	Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image Shumian Xin,Neal Wadhwa,Tianfan Xue,Jonathan T. Barron,Pratul Srinivasan,Jiawen Chen,Ioannis Gkioulekas,Rahul Garg ICCV, 2021 (Oral Presentation) project page /code /arXiv We deblur dual-pixel images by representing the scene as a multiplane image and carefully considering dual-pixel optics in an optimization framework.
	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 We recover relightable NeRF-like models using neural approximations of expensive visibility integrals, so we can simulate complex volumetric light transport during training.
Your browser does not support the video tag.	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 We use meta-learning to find weight initializations for coordinate-based MLPs that allow them to converge faster and generalize better.
Your browser does not support the video tag.	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 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 Mapping input coordinates with simple Fourier features before passing them to a fully-connected network enables the network to learn much higher-frequency functions.
Your browser does not support the video tag.	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 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 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.
Your browser does not support the video tag.	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 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 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 SIGGRAPH, 2019 project page /arXiv /video /code 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 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 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 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 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 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 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 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 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 We develop a segmentation algorithm to quantify the shape of retinal layers in OCT images that is robust to deformations due to disease.

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Last updated December 2023.