3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching

TL;DR

This paper introduces an appearance-based method for 3D hand pose estimation from Kinect point clouds, effectively handling both isolated hand and hand-object interactions using a modified ICP algorithm without relying on color or normal data.

Contribution

The paper presents a novel ICP-based framework for hand pose estimation from pure point clouds, applicable to various depth sensors and capable of handling complex hand-object interactions.

Findings

Effective pose estimation in both hand-only and hand-object scenarios

Applicable to different depth sensors without additional data

Robust alignment using modified ICP algorithm

Abstract

We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera.