Search Results for author: Ricardo Martin-Brualla

Found 25 papers, 9 papers with code

CAT3D: Create Anything in 3D with Multi-View Diffusion Models

no code implementations16 May 2024 Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T. Barron, Ben Poole

Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene.

3D Reconstruction

CamP: Camera Preconditioning for Neural Radiance Fields

no code implementations21 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.

SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates

no code implementations CVPR 2023 Mikaela Angelina Uy, Ricardo Martin-Brualla, Leonidas Guibas, Ke Li

To address this issue, we introduce SCADE, a novel technique that improves NeRF reconstruction quality on sparse, unconstrained input views for in-the-wild indoor scenes.

3D Reconstruction Monocular Depth Estimation +1

LFM-3D: Learnable Feature Matching Across Wide Baselines Using 3D Signals

no code implementations22 Mar 2023 Arjun Karpur, Guilherme Perrotta, Ricardo Martin-Brualla, Howard Zhou, André Araujo

Finding localized correspondences across different images of the same object is crucial to understand its geometry.


Novel View Synthesis with Diffusion Models

no code implementations6 Oct 2022 Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, Mohammad Norouzi

We demonstrate that stochastic conditioning significantly improves the 3D consistency of a naive sampler for an image-to-image diffusion model, which involves conditioning on a single fixed view.

Denoising Novel View Synthesis

HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields

2 code implementations24 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.

Novel View Synthesis

FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling

no code implementations17 Apr 2021 Christopher Xie, Keunhong Park, Ricardo Martin-Brualla, Matthew Brown

We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images.


Neural RGB-D Surface Reconstruction

2 code implementations CVPR 2022 Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, Justus Thies

Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR.

Image Generation Mixed Reality +2

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

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.

IBRNet: Learning Multi-View Image-Based Rendering

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.

Neural Rendering Novel View Synthesis

ShaRF: Shape-conditioned Radiance Fields from a Single View

no code implementations17 Feb 2021 Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari

We demonstrate in several experiments the effectiveness of our approach in both synthetic and real images.

Disentanglement Object

Time-Travel Rephotography

1 code implementation22 Dec 2020 Xuan Luo, Xuaner Zhang, Paul Yoo, Ricardo Martin-Brualla, Jason Lawrence, Steven M. Seitz

Many historical people were only ever captured by old, faded, black and white photos, that are distorted due to the limitations of early cameras and the passage of time.

Colorization Denoising +1

Nerfies: Deformable Neural Radiance Fields

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.

3D Human Reconstruction

GeLaTO: Generative Latent Textured Objects

no code implementations ECCV 2020 Ricardo Martin-Brualla, Rohit Pandey, Sofien Bouaziz, Matthew Brown, Dan B. Goldman

Accurate modeling of 3D objects exhibiting transparency, reflections and thin structures is an extremely challenging problem.

NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

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.

State of the Art on Neural Rendering

no code implementations8 Apr 2020 Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B. Goldman, Michael Zollhöfer

Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e. g., by the integration of differentiable rendering into network training.

BIG-bench Machine Learning Image Generation +2

KeystoneDepth: Visualizing History in 3D

no code implementations21 Aug 2019 Xuan Luo, Yanmeng Kong, Jason Lawrence, Ricardo Martin-Brualla, Steve Seitz

This paper introduces the largest and most diverse collection of rectified stereo image pairs to the research community, KeystoneDepth, consisting of tens of thousands of stereographs of historical people, events, objects, and scenes between 1860 and 1963.

Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning

no code implementations CVPR 2019 Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello

The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.

Neural Rerendering in the Wild

no code implementations CVPR 2019 Moustafa Meshry, Dan B. Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, Ricardo Martin-Brualla

Starting from internet photos of a tourist landmark, we apply traditional 3D reconstruction to register the photos and approximate the scene as a point cloud.

3D Reconstruction

LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering

no code implementations12 Nov 2018 Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Julien Valentin, Sameh Khamis, Philip Davidson, Anastasia Tkach, Peter Lincoln, Adarsh Kowdle, Christoph Rhemann, Dan B. Goldman, Cem Keskin, Steve Seitz, Shahram Izadi, Sean Fanello

We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.

Denoising Super-Resolution

3D Time-lapse Reconstruction from Internet Photos

no code implementations ICCV 2015 Ricardo Martin-Brualla, David Gallup, Steven M. Seitz

Given an Internet photo collection of a landmark, we compute a 3D time-lapse video sequence where a virtual camera moves continuously in time and space.

Cannot find the paper you are looking for? You can Submit a new open access paper.