# A Comparison of Directional Distances for Hand Pose Estimation

## TL;DR

This paper introduces a new dataset and benchmarking protocol for 3D hand pose estimation that isolates individual frame errors, enabling more precise evaluation of directional distance methods like the generalized Chamfer distance.

## Contribution

It proposes a novel benchmarking protocol and dataset for more accurate evaluation of hand pose estimation methods, focusing on directional distances and their parameter settings.

## Key findings

- Identifies the best performing directional distance method for silhouette-based 3D hand tracking.
- Provides a new protocol that reduces error accumulation in benchmarking.
- Offers insights into parameter tuning for different distance measures.

## Abstract

Benchmarking methods for 3d hand tracking is still an open problem due to the difficulty of acquiring ground truth data. We introduce a new dataset and benchmarking protocol that is insensitive to the accumulative error of other protocols. To this end, we create testing frame pairs of increasing difficulty and measure the pose estimation error separately for each of them. This approach gives new insights and allows to accurately study the performance of each feature or method without employing a full tracking pipeline. Following this protocol, we evaluate various directional distances in the context of silhouette-based 3d hand tracking, expressed as special cases of a generalized Chamfer distance form. An appropriate parameter setup is proposed for each of them, and a comparative study reveals the best performing method in this context.