Search Results for author: David F. Fouhey

Found 19 papers, 8 papers with code

SynthIA: A Synthetic Inversion Approximation for the Stokes Vector Fusing SDO and Hinode into a Virtual Observatory

no code implementations27 Aug 2021 Richard E. L. Higgins, David F. Fouhey, Spiro K. Antiochos, Graham Barnes, Mark C. M. Cheung, J. Todd Hoeksema, KD Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi

Both NASA's Solar Dynamics Observatory (SDO) and the JAXA/NASA Hinode mission include spectropolarimetric instruments designed to measure the photospheric magnetic field.

PixelSynth: Generating a 3D-Consistent Experience from a Single Image

1 code implementation ICCV 2021 Chris Rockwell, David F. Fouhey, Justin Johnson

Recent advancements in differentiable rendering and 3D reasoning have driven exciting results in novel view synthesis from a single image.

Novel View Synthesis

Collision Replay: What Does Bumping Into Things Tell You About Scene Geometry?

no code implementations3 May 2021 Alexander Raistrick, Nilesh Kulkarni, David F. Fouhey

At the heart of our approach is the idea of collision replay, where we use examples of a collision to provide supervision for observations at a past frame.

Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification

1 code implementation31 Mar 2021 Richard E. L. Higgins, David F. Fouhey, Dichang Zhang, Spiro K. Antiochos, Graham Barnes, J. Todd Hoeksema, K. D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi

The Helioseismic and Magnetic Imager (HMI) onboard NASA's Solar Dynamics Observatory (SDO) produces estimates of the photospheric magnetic field which are a critical input to many space weather modelling and forecasting systems.

Planar Surface Reconstruction from Sparse Views

1 code implementation ICCV 2021 Linyi Jin, Shengyi Qian, Andrew Owens, David F. Fouhey

The paper studies planar surface reconstruction of indoor scenes from two views with unknown camera poses.

Full-Body Awareness from Partial Observations

no code implementations ECCV 2020 Chris Rockwell, David F. Fouhey

There has been great progress in human 3D mesh recovery and great interest in learning about the world from consumer video data.

Associative3D: Volumetric Reconstruction from Sparse Views

1 code implementation ECCV 2020 Shengyi Qian, Linyi Jin, David F. Fouhey

This information is then jointly reasoned over to produce the most likely explanation of the scene.

3D Volumetric Reconstruction

Understanding Human Hands in Contact at Internet Scale

1 code implementation CVPR 2020 Dandan Shan, Jiaqi Geng, Michelle Shu, David F. Fouhey

Hands are the central means by which humans manipulate their world and being able to reliably extract hand state information from Internet videos of humans engaged in their hands has the potential to pave the way to systems that can learn from petabytes of video data.

Novel Object Viewpoint Estimation through Reconstruction Alignment

1 code implementation CVPR 2020 Mohamed El Banani, Jason J. Corso, David F. Fouhey

Our key insight is that although we do not have an explicit 3D model or a predefined canonical pose, we can still learn to estimate the object's shape in the viewer's frame and then use an image to provide our reference model or canonical pose.

Image-to-Image Translation Viewpoint Estimation

Articulation-aware Canonical Surface Mapping

1 code implementation CVPR 2020 Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani

We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canonical template shape, and 2) inferring the articulation and pose of the template corresponding to the input image.

A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission

no code implementations11 Mar 2019 Richard Galvez, David F. Fouhey, Meng Jin, Alexandre Szenicer, Andrés Muñoz-Jaramillo, Mark C. M. Cheung, Paul J. Wright, Monica G. Bobra, Yang Liu, James Mason, Rajat Thomas

In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research.

From Lifestyle Vlogs to Everyday Interactions

no code implementations CVPR 2018 David F. Fouhey, Wei-cheng Kuo, Alexei A. Efros, Jitendra Malik

A major stumbling block to progress in understanding basic human interactions, such as getting out of bed or opening a refrigerator, is lack of good training data.

Future prediction

From Images to 3D Shape Attributes

no code implementations20 Dec 2016 David F. Fouhey, Abhinav Gupta, Andrew Zisserman

Our first objective is to infer these 3D shape attributes from a single image.

3D Shape Attributes

no code implementations CVPR 2016 David F. Fouhey, Abhinav Gupta, Andrew Zisserman

In this paper we investigate 3D attributes as a means to understand the shape of an object in a single image.

Learning a Predictable and Generative Vector Representation for Objects

2 code implementations29 Mar 2016 Rohit Girdhar, David F. Fouhey, Mikel Rodriguez, Abhinav Gupta

The network consists of two components: (a) an autoencoder that ensures the representation is generative; and (b) a convolutional network that ensures the representation is predictable.

In Defense of the Direct Perception of Affordances

no code implementations5 May 2015 David F. Fouhey, Xiaolong Wang, Abhinav Gupta

The field of functional recognition or affordance estimation from images has seen a revival in recent years.

Designing Deep Networks for Surface Normal Estimation

no code implementations CVPR 2015 Xiaolong Wang, David F. Fouhey, Abhinav Gupta

We show by incorporating several constraints (man-made, manhattan world) and meaningful intermediate representations (room layout, edge labels) in the architecture leads to state of the art performance on surface normal estimation.

Scene Understanding

Predicting Object Dynamics in Scenes

no code implementations CVPR 2014 David F. Fouhey, C. L. Zitnick

Given a static scene, a human can trivially enumerate the myriad of things that can happen next and characterize the relative likelihood of each.

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