@inproceedings{003897e29c544149a3a3e834c4d7dd36,
title = "The WoodScape iToF NFL LiDAR dataset",
abstract = "Indirect-time-of-flight (iToF) LiDAR sensors based on Amplitude-Modulated Continuous Wave (AMCW) technology and CMOS technology offer a cost-efficient solution for generating dense, accurate point clouds in near-field applications such as parking and navigating traffic jams. However, due to technology limitations, iToF sensors face critical challenges such as depth range limitations and ambiguities, motion blur noise, and multi-path interference. In this dataset, we provide the recorded data for a single front view Near Field (NFL) iToF LiDAR, with point cloud frames containing limitations like depth ambiguity, motion blur, and multipath interference, the corrected ground truth point cloud, and additional inputs, including grayscale images and signal-to-noise ratio frames for defective frames. This dataset aims to help researchers develop solutions to overcome the technology limitations of the iToF sensors and encourage their use in autonomous mobile robots and automotive-grade applications.",
keywords = "ambiguity, blur, completion, computer vision, depth, indirect, LiDAR, motion, Near-field, pattern recognition, time-of-flight",
author = "Mena Nagiub and Thorsten Beuth and Ganesh Sistu and Heinrich Gotzig and Ciaran Eising",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2025 ; Conference date: 27-10-2025 Through 28-10-2025",
year = "2025",
doi = "10.1109/ICVES65691.2025.11376416",
language = "English",
series = "Proceedings of the 2025 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "21--27",
booktitle = "Proceedings of the 2025 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2025",
}