RateAR Dataset

This repository accompanies the conference paper "Harnessing Vision-Language Models for Perceptual Quality Assessment and Autonomous Content Adjustment in Augmented Reality", published in the 32nd ACM Symposium on Virtual Reality Software and Technology (VRST `26). It introduces RateAR, a dataset of 321 AR images and 112 AR videos collected using various hardware platforms and user scenarios.

Data Collection Process

AR Platforms: AR samples are sourced from a range of devices and scenarios. 321 AR images are collected from DiverseAR+ [Duan et al., 2025], selected for their varying rendering qualities in virtual content placement, shadow, and size. All RateAR image instances were captured using Android smartphones (250), Apple Vision Pro (40), and Microsoft HoloLens 2 (31). A diverse collection of environments are featured, including bedrooms, kitchens, living rooms, medical offices, reading rooms, research labs, and study rooms. The 112 AR videos were captured by us to represent a similar distribution of quality feature variations, showcasing settings such as basements, bedrooms, living rooms, and research labs. All RateAR videos are recorded using Meta Quest 3.

Context-Dependent Scenarios: Alongside common settings such as bedrooms and kitchens, where assessing visual factors primarily relies on general, publicly shared knowledge, RateAR also includes scenes that require context-dependent reasoning for evaluation. For example, medical settings feature virtual organ models placed in anatomically accurate locations on human subjects or 3D-printed models (e.g., a skull). In these scenarios, the placement plausibility and size appropriateness of virtual content is most critical, while elements like shadow realism are less relevant for perceived quality. Additionally, we include scenarios that capture dynamic user interactions, such as a hand shown bouncing a virtual basketball. As a result, some examples with observed floating AR content may be contextually appropriate, therefore receiving a high placement plausibility score.

Human Subjective Quality Score Labeling: Four graduate students with experience designing, developing, and evaluating AR systems rated three features (placement plausibility, size appropriateness, and shadow realism) using a Python notebook GUI. Before labeling samples, the annotators discussed the target quality features and expectations for handling nuanced edge cases, such as what score to assign when a virtual object without an accompanying shadow was visually appropriate (e.g., anatomical models). As needed, subsequent consensus meetings remediated annotator conceptual disagreements. Modeled after the International Telecommunication Union's (ITU) Absolute Category Rating guidelines, a 5-point discrete scale was employed. An assignment of 1 signifies poorly rendered feature quality, while a score of 5 indicates good or exceptional visual appearance. After the initial annotation period, we recruited a fifth student with a background in image analysis applications to expand the label set to five ratings per sample. The final annotator received reference materials summarizing the earlier group's discussions to align evaluation standards. All annotators assigned quality labels for each AR content factor, per sample, resulting in 6,495 annotations. The mean opinion scores (MOSs) are reported and used as final ratings in our evaluations.

Inter-Annotator Reliability: To measure inter-annotator reliability, we computed the intraclass correlation coefficients (ICC) for each labeled quality feature based on a two-way random-effects model, average measures, and absolute agreement using the Pingouin open-source Python package. The model for placement plausibility yielded ICC(2,5) = .95, 95% CI[.94, .96], F(432, 1728) = 22.20, p < .001, supporting excellent reliability. Similarly, we observe comparable excellent reliability for the size appropriateness labels, with an ICC(2,5) = .94, 95% CI[.93, .95], F(432, 1728) = 16.55, p < .001. Lastly, the shadow realism annotation analysis returns an ICC(2,5) = .90, 95% CI[.87, .92], F(432, 1728) = 11.15, p < .001, indicating good to excellent reliability.

RateAR Hierarchical Structure

The dataset follows the hierarchical file structure shown below:

dataset
└───rateAR_images
│   └───rateAR_image_metadata_mos.csv
│   └───AVP
│   │   └───AVP_can
│   │   │   └───AVP_can_1_ar.png
│   │   │   ...
│   │   ...
│   └───andriod
│   ...
└───rateAR_videos
│   └───rateAR_video_metadata_mos.csv
│   └───livingroom
│   │   └───livingroom_tire_1_1_1.mp4
│   │   ...
│   └───dorm
│   ...

Dataset Download Options

1. Python (huggingface_hub)

Using a Python script, directly download the entire dataset (including all subdirectories) using the official Hugging Face Hub API:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="I3TDataset/RateAR", 
    repo_type="dataset", 
    local_dir="./RateAR", # Save dataset within working dir
)

2. Hugging Face CLI

The RateAR dataset can also be downloaded locally using Hugging Face's official command-line interface:

1. Intall the Hugging Face Hub tool

   pip install huggingface_hub
   

2. Download the dataset to the target local directory (e.g., "./RateAR")

   hf download I3TDataset/RateAR --repo-type dataset --local-dir ./RateAR
   

Citation

If you use the RateAR dataset in any capacity, please cite:

@inproceedings{rotondo2026ratear,
  author = {Rotondo, Elias and Duan, Lin and Xiu, Yanming and Eom, Sangjun and Li, Conrad, and Gorlatova, Maria},
  title = {Harnessing Vision-Language Models for Perceptual Quality Assessment and Autonomous Content Adjustment in Augmented Reality},
  year = {2026},
  isbn = {9798400728112},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3822517.3848685},
  doi = {10.1145/3822517.3848685},
  booktitle = {Proceedings of the 2026 32nd ACM Symposium on Virtual Reality Software and Technology},
  location = {Sendai, Japan},
  series = {VRST '26}
}

Contributors

The contributors of the dataset are Elias Rotondo, Lin Duan, Yanming Xiu, Sangjun Eom, Conrad Li, and Maria Gorlatova. For questions relating to this repository or the associated paper, please contact:

• Elias Rotondo - eli [DOT] rotondo [AT] duke [DOT] edu

Acknowledgments

We thank the participants of our user study for their invaluable help in this research and Tanish Pentakota for assistance implementing baselines.

This work was supported in part by NSF grants CSR-2312760, CNS-2112562, and IIS-2231975, NSF CAREER Award IIS-2046072, NSF NAIAD Award 2332744, a CISCO Research Award, a Meta Research Award, Defense Advanced Research Projects Agency Young Faculty Award HR0011-24-1-0001, and the Army Research Laboratory under Cooperative Agreement Number W911NF-23-2-0224. The views and conclusions contained in these works are those of the authors. They should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency, the Army Research Laboratory, or the U.S. Government. This resource has been approved for public release; distribution is unlimited. No official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes, notwithstanding any copyright notation herein.