We introduce Princeton365, a large-scale diverse dataset of 365 videos with accurate camera pose. Our dataset bridges the gap between accuracy and data diversity in current SLAM benchmarks by introducing a novel ground truth collection framework that leverages calibration boards and a 360-camera. We collect indoor, outdoor, and object scanning videos with synchronized monocular and stereo RGB video outputs as well as IMU. We further propose a new scene scale-aware evaluation metric for SLAM based on the the optical flow induced by the camera pose estimation error. In contrast to the current metrics, our new metric allows for comparison between the performance of SLAM methods across scenes as opposed to existing metrics such as Average Trajectory Error (ATE), allowing researchers to analyze the failure modes of their methods. We also propose a challenging Novel View Synthesis benchmark that covers cases not covered by current NVS benchmarks, such as fully non-Lambertian scenes with 360-degree camera trajectories.
Karhan Kayan*,
Stamatis Alexandropoulos*,
Rishabh Jain,
Yiming Zuo,
Erich Liang,
Jia Deng
* Equal contribution (random order)
International Conference on Computer Vision (ICCV), 2025
If you use our benchmark, data, or method in your work, please cite our paper:
@misc{princeton365,
title={Princeton365: A Diverse Dataset with Accurate Camera Pose},
author={Karhan Kayan and Stamatis Alexandropoulos and Rishabh Jain and Yiming Zuo and Erich Liang and Jia Deng},
year={2025},
eprint={2506.09035},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.09035},
}
This work was partially supported by the National Science Foundation, Onassis Foundation and a gift from Meta Reality Labs.