PittsburghV1, France, Analysis, bibRecord, 000115

Understanding Everyday Hands in Action from RGB-D Images

Identifieur interne : 000115 ( France/Analysis ); précédent : 000114; suivant : 000116

Understanding Everyday Hands in Action from RGB-D Images

Auteurs : Gregory Rogez [France] ; James Supancic [États-Unis] ; Deva Ramanan [États-Unis]

Source :

RBID : Hal:hal-01237011

Abstract

We analyze functional manipulations of handheld objects, formalizing the problem as one of fine-grained grasp classification. To do so, we make use of a recently developed fine-grained taxonomy of human-object grasps. We introduce a large dataset of 12000 RGB-D images covering 71 everyday grasps in natural interactions. Our dataset is different from past work (typically addressed from a robotics perspective) in terms of its scale, diversity, and combination of RGB and depth data. From a computer-vision perspective , our dataset allows for exploration of contact and force prediction (crucial concepts in functional grasp analysis) from perceptual cues. We present extensive experimental results with state-of-the-art baselines, illustrating the role of segmentation, object context, and 3D-understanding in functional grasp analysis. We demonstrate a near 2X improvement over prior work and a naive deep baseline, while pointing out important directions for improvement.

Url:

https://hal.inria.fr/hal-01237011

DOI: 10.1109/ICCV.2015.443

Affiliations:

Links toward previous steps (curation, corpus...)

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to stream Main, to step Merge: 000169
to stream Main, to step Curation: 000169
to stream Main, to step Exploration: 000169
to stream France, to step Extraction: 000115

Links to Exploration step

Hal:hal-01237011

Le document en format XML

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<front><div type="abstract" xml:lang="en">We analyze functional manipulations of handheld objects, formalizing the problem as one of fine-grained grasp classification. To do so, we make use of a recently developed fine-grained taxonomy of human-object grasps. We introduce a large dataset of 12000 RGB-D images covering 71 everyday grasps in natural interactions. Our dataset is different from past work (typically addressed from a robotics perspective) in terms of its scale, diversity, and combination of RGB and depth data. From a computer-vision perspective , our dataset allows for exploration of contact and force prediction (crucial concepts in functional grasp analysis) from perceptual cues. We present extensive experimental results with state-of-the-art baselines, illustrating the role of segmentation, object context, and 3D-understanding in functional grasp analysis. We demonstrate a near 2X improvement over prior work and a naive deep baseline, while pointing out important directions for improvement.</div>
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   |texte=   Understanding Everyday Hands in Action from RGB-D Images
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Understanding Everyday Hands in Action from RGB-D Images

Understanding Everyday Hands in Action from RGB-D Images

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