DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation

arXiv:2403.13405v1 Announce Type: new
Abstract: Depth-based 3D hand pose estimation is an important but challenging research task in human-machine interaction community. Recently, dense regression methods have attracted increasing attention in 3D hand pose estimation task, which provide a low computational burden and high accuracy regression way by densely regressing hand joint offset maps. However, large-scale regression offset values are often affected by noise and outliers, leading to a significant drop in accuracy. To tackle this, we re-formulate 3D hand pose …

abstract accuracy arxiv attention community computational cs.ai cs.cv human human-machine interaction low machine network ordinal regression research type