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

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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