USCILab3D: A Large-scale, Long-term, Semantically Annotated Outdoor Dataset
Kiran Lekkala, Henghui Bao, Peixu Cai, Wei Zer Lim, Chen Liu, Laurent Itti
Abstract
In this paper, we introduce the \textbf{USCILab3D dataset}, a large-scale, annotated outdoor dataset designed for versatile applications across multiple domains, including computer vision, robotics, and machine learning. The dataset was acquired using a mobile robot equipped with 5 cameras and a 32-beam, $360^{\circ}$ scanning LIDAR. The robot was teleoperated, over the course of a year and under a variety of weather and lighting conditions, through a rich variety of paths within the USC campus (229 acres = $\sim 92.7$ hectares). The raw data was annotated using state-of-the-art large foundation models, and processed to provide multi-view imagery, 3D reconstructions, semantically-annotated images and point clouds (267 semantic categories), and text descriptions of images and objects within. The dataset also offers a diverse array of complex analyses using pose-stamping and trajectory data. In sum, the dataset offers 1.4M point clouds and 10M images ($\sim 6$TB of data). Despite covering a narrower geographical scope compared to a whole-city dataset, our dataset prioritizes intricate intersections along with denser multi-view scene images and semantic point clouds, enabling more precise 3D labelling and facilitating a broader spectrum of 3D vision tasks. For data, code and more details, please visit our website.
BibTeX
@inproceedings{
lekkala2024uscilabd,
title={{USCIL}ab3D: A Large-scale, Long-term, Semantically Annotated Outdoor Dataset},
author={Kiran Lekkala and Henghui Bao and Peixu Cai and Wei Zer Lim and Chen Liu and Laurent Itti},
booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2024},
url={https://openreview.net/forum?id=mbAgKSfhjM}
}