ARPOV: Expanding Visualization of Object Detection in AR with Panoramic Mosaic Stitching
Erin McGowan, Ethan Brewer, Claudio Silva

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…
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Taxonomy
Topics3D Surveying and Cultural Heritage · Augmented Reality Applications · 3D Shape Modeling and Analysis
MethodsVisual Analytics
