Výsledky vyhledávání - "Chris Sweeney"

LISA: Learning Implicit Shape and Appearance of Hands

Autor: Enric Corona, Tomas Hodan, Minh Vo, Francesc Moreno-Noguer, Chris Sweeney, Richard Newcombe, Lingni Ma

This paper proposes a do-it-all neural model of human hands, named LISA. The model can capture accurate hand shape and appearance, generalize to arbitrary hand subjects, provide dense surface correspondences, be reconstructed from images in the wild

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5bf20ab6585b4733a08fd49f0b79ccb
http://arxiv.org/abs/2204.01695

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Self-supervised Neural Articulated Shape and Appearance Models

Autor: Fangyin Wei, Rohan Chabra, Lingni Ma, Christoph Lassner, Michael Zollhoefer, Szymon Rusinkiewicz, Chris Sweeney, Richard Newcombe, Mira Slavcheva

Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches has focused on static objects, dynamic objects, especially with controll

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::599fc14e07ea10319337d548375edbb5

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Scalable Scene Flow from Point Clouds in the Real World

Autor: Philipp Jund, Chris Sweeney, Nichola Abdo, Zhifeng Chen, Jonathon Shlens

Autonomous vehicles operate in highly dynamic environments necessitating an accurate assessment of which aspects of a scene are moving and where they are moving to. A popular approach to 3D motion estimation, termed scene flow, is to employ 3D point

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3013e54655a5d6d0894a082ff48c0476

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Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learning

Autor: Chris Sweeney, Maryam Najafian

Publikováno v: FAT*

The use of word embedding models in sentiment analysis has gained a lot of traction in the Natural Language Processing (NLP) community. However, many inherently neutral word vectors describing demographic identity have unintended implicit correlation

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::14fab7f643ea92d83558f5ca8d4439e4
https://doi.org/10.1145/3351095.3372837

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StereoDRNet: Dilated Residual StereoNet

Autor: Rohan Chabra, Chris Sweeney, Henry Fuchs, Julian Straub, Richard Newcombe

Publikováno v: CVPR

We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reconstruction of a scene. Our proposed depth refinement architecture, pre

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c3d4f340508fae952df3464104b70846
https://doi.org/10.1109/cvpr.2019.01206

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A Supervised Approach to Predicting Noise in Depth Images

Autor: Chris Sweeney, Russ Tedrake, Greg Izatt

Publikováno v: MIT web domain
ICRA

Modern robotic systems are very complex and need to be tested in simulations with detailed sensor noise models to effectively verify robotic behavior. Depth imagery in particular comes with significant noise in the form of scene-dependent pixel-wise

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7db31544a38aa87a1a410fc3c03c4272
https://hdl.handle.net/1721.1/137637

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