ARPOV: Expanding Visualization of Object Detection in AR with Panoramic Mosaic Stitching

TL;DR

ARPOV is an interactive visualization tool that enhances understanding of object detection in AR videos by using panoramic stitching and filtering, aiding debugging and performance analysis.

Contribution

The paper introduces ARPOV, a novel visualization tool that expands AR video analysis through panoramic stitching and interactive features for model debugging.

Findings

Effective panorama stitching improves environmental context understanding.

Filtering undesirable frames enhances visualization clarity.

Expert interviews validate the tool's usefulness for model debugging.

Abstract

As the uses of augmented reality (AR) become more complex and widely available, AR applications will increasingly incorporate intelligent features that require developers to understand the user's behavior and surrounding environment (e.g. an intelligent assistant). Such applications rely on video captured by an AR headset, which often contains disjointed camera movement with a limited field of view that cannot capture the full scope of what the user sees at any given time. Moreover, standard methods of visualizing object detection model outputs are limited to capturing objects within a single frame and timestep, and therefore fail to capture the temporal and spatial context that is often necessary for various domain applications. We propose ARPOV, an interactive visual analytics tool for analyzing object detection model outputs tailored to video captured by an AR headset that maximizes user understanding of model performance. The proposed tool leverages panorama stitching to expand the view of the environment while automatically filtering undesirable frames, and includes interactive features that facilitate object detection model debugging. ARPOV was designed as part of a collaboration between visualization researchers and machine learning and AR experts; we validate our design choices through interviews with 5 domain experts.