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

posted @ 2018-03-04 16:05  Blueprintf  阅读(310)  评论(0编辑  收藏