Abstract
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. Please visit this https URL for the dataset, code, videos, and submission.
Abstract (translated)
我们介绍了Princeton365，这是一个包含365个视频的大规模多样化数据集，并且每个视频都具有精确的相机姿态。我们的数据集通过引入一个新颖的真实情况收集框架来弥合当前SLAM基准测试中准确性与数据多样性之间的差距，该框架利用校准板和360度摄像头进行工作。我们采集了室内、室外以及物体扫描视频，包括同步的单目和立体RGB视频输出及IMU（惯性测量单元）信息。 此外，我们还提出了一种新的基于场景尺度感知的SLAM评估指标，该指标是根据相机姿态估计误差所引起的光流来衡量。与现有的指标如平均轨迹错误（ATE）相比，我们的新度量标准允许跨不同场景比较SLAM方法的性能，这使得研究者能够分析其方法的失效模式。 我们还提出了一项具有挑战性的新型视图合成基准测试，该测试涵盖了当前NVS（Novel View Synthesis，新颖视角合成）基准所未涉及的情况，例如全非朗伯场景以及360度相机轨迹。请访问此[链接](https://example.com)获取数据集、代码、视频和提交指南等相关信息。
URL
https://arxiv.org/abs/2506.09035
