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

arXiv: https://arxiv.org/abs/2410.01055 · HTML: https://mlsanity.com/paper/2410.01055/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [Multiple Object Tracking with Correlation Learning](https://mlsanity.com/paper/2104.03541/) (2021)
- [HoloLens 2 Research Mode as a Tool for Computer Vision Research](https://mlsanity.com/paper/2008.11239/) (2020)
- [ARGUS: Visualization of AI-Assisted Task Guidance in AR](https://mlsanity.com/paper/2308.06246/) (2023)
- [Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks](https://mlsanity.com/paper/2008.13419/) (2020)

## Abstract (arXiv)

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.