TY - GEN
T1 - Cirrus
T2 - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
AU - Wang, Ze
AU - Ding, Sihao
AU - Li, Ying
AU - Fenn, Jonas
AU - Roychowdhury, Sohini
AU - Wallin, Andreas
AU - Martin, Lane
AU - Ryvola, Scott
AU - Sapiro, Guillermo
AU - Qiu, Qiang
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - In this paper, we introduce Cirrus, a new long-range bi-pattern LiDAR public dataset for autonomous driving tasks such as 3D object detection, critical to highway driving and timely decision making. Our platform is equipped with a high-resolution video camera and a pair of LiDAR sensors with a 250-meter effective range, which is significantly longer than existing public datasets. We record paired point clouds simultaneously using both Gaussian and uniform scanning patterns. Point density varies significantly across such a long range, and different scanning patterns further diversify object representation in LiDAR. In Cirrus, eight categories of objects are exhaustively annotated in the LiDAR point clouds for the entire effective range. To illustrate the kind of studies supported by this new dataset, we introduce LiDAR model adaptation across different ranges, scanning patterns, and sensor devices. Promising results show the great potential of this new dataset to the robotics and computer vision communities.
AB - In this paper, we introduce Cirrus, a new long-range bi-pattern LiDAR public dataset for autonomous driving tasks such as 3D object detection, critical to highway driving and timely decision making. Our platform is equipped with a high-resolution video camera and a pair of LiDAR sensors with a 250-meter effective range, which is significantly longer than existing public datasets. We record paired point clouds simultaneously using both Gaussian and uniform scanning patterns. Point density varies significantly across such a long range, and different scanning patterns further diversify object representation in LiDAR. In Cirrus, eight categories of objects are exhaustively annotated in the LiDAR point clouds for the entire effective range. To illustrate the kind of studies supported by this new dataset, we introduce LiDAR model adaptation across different ranges, scanning patterns, and sensor devices. Promising results show the great potential of this new dataset to the robotics and computer vision communities.
UR - http://www.scopus.com/inward/record.url?scp=85121734145&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121734145&partnerID=8YFLogxK
U2 - 10.1109/ICRA48506.2021.9561267
DO - 10.1109/ICRA48506.2021.9561267
M3 - Conference contribution
AN - SCOPUS:85121734145
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5744
EP - 5750
BT - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 30 May 2021 through 5 June 2021
ER -