Understanding Everyday Hands in Action from RGB-D Images
icon 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.

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Text Reference

Grégory Rogez, James Steven Supan\vc i\vc  III, and Deva Ramanan. Understanding everyday hands in action from rgb-d images. In IEEE International Conference on Computer Vision. 2015.

BibTeX Reference

    author = "Rogez, Gr{\'e}gory and Supan{\vc}i{\vc} III, James Steven and Ramanan, Deva",
    booktitle = "IEEE International Conference on Computer Vision",
    title = "Understanding Everyday Hands in Action from RGB-D Images",
    year = "2015",
    tag = "people"