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  • Paper Reading: 3D Hand Pose Estimation: From Current Achievementsto Future Goals (CVPR 2018)

    文章在本文中尝试去回答两个问题:

    1、What is the current state of 3D hand pose estimation?

    2、And, what are the next challenges that need to be tackled?

    结合了2017年的竞赛,Hand In the Million Challenge(HIM2017)

    three tasks : single frame, tracking, during object interaction

    得到了以下结论:

    (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [40, 150] degrees, but it is far from being solvedforextremeviewpoints;

    (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structureofthedepthdata;

    (3) Discriminative methods generalize poorly to unseen hand shapes

    (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints.

    cross-benchmark testing is poor : view point, hand shape, self-occlusion, occlusion caused by objects.

    network architectures, preprocessing strategies, data representations

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  • 原文地址:https://www.cnblogs.com/blueprintf/p/8504979.html
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