XR Reality Check: What Commercial Devices Deliver for Spatial Tracking

TL;DR

This paper introduces a standardized benchmarking framework for evaluating spatial tracking accuracy in XR devices, revealing significant intra- and inter-device performance variations influenced by environmental and hardware factors.

Contribution

It presents the first comprehensive empirical benchmarking platform for XR tracking devices, including a new testbed, datasets, and open-source evaluation tools.

Findings

Substantial intra-device error variation up to 101% in featureless environments.

Tracking accuracy correlates with visual conditions and motion dynamics.

Performance disparities of up to 2.8× between different XR devices linked to hardware differences.

Abstract

Inaccurate spatial tracking in extended reality (XR) devices leads to virtual object jitter, misalignment, and user discomfort, fundamentally limiting immersive experiences and natural interactions. In this work, we introduce a novel testbed that enables simultaneous, synchronized evaluation of multiple XR devices under identical environmental and kinematic conditions. Leveraging this platform, we present the first comprehensive empirical benchmarking of five state-of-the-art XR devices across 16 diverse scenarios. Our results reveal substantial intra-device performance variation, with individual devices exhibiting up to 101\% increases in error when operating in featureless environments. We also demonstrate that tracking accuracy strongly correlates with visual conditions and motion dynamics. We also observe significant inter-device disparities, with performance differences of up to 2.8$\times$, which are closely linked to hardware specifications such as sensor configurations and dedicated processing units. Finally, we explore the feasibility of substituting a motion capture system with the Apple Vision Pro as a practical ground truth reference. While the Apple Vision Pro delivers highly accurate relative pose error estimates ($R^2 = 0.830$), its absolute pose error estimation remains limited ($R^2 = 0.387$), highlighting both its potential and its constraints for rigorous XR evaluation. This work establishes the first standardized framework for comparative XR tracking evaluation, providing the research community with reproducible methodologies, comprehensive benchmark datasets, and open-source tools that enable systematic analysis of tracking performance across devices and conditions, thereby accelerating the development of more robust spatial sensing technologies for XR systems.