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DurLAR : A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications : DurLAR_20211209_S_CAM [dataset] Open Access

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is equipped with a high resolution 128 channel LiDAR, a 2MPix stereo camera, a lux meter and a GNSS/INS system. Ambient and reflectivity images are made available along with the LiDAR point clouds to facilitate multi-modal use of concurrent ambient and reflectivity scene information. Leveraging DurLAR, with a resolution exceeding that of prior benchmarks, we consider the task of monocular depth estimation and use this increased availability of higher resolution, yet sparse ground truth scene depth information to propose a novel joint supervised/selfsupervised loss formulation. We compare performance over both our new DurLAR dataset, the established KITTI benchmark and the Cityscapes dataset. Our evaluation shows our joint use supervised and self-supervised loss terms, enabled via the superior ground truth resolution and availability within DurLAR improves the quantitative and qualitative performance of leading contemporary monocular depth estimation approaches (RMSE = 3.639, SqRel = 0.936).

Descriptions

Resource type
Dataset
Contributors
Creator: Breckon, Toby P. 1
Creator: Li, Li 1
Editor: Li, Li 1
Creator: Ismail, Khalid 1
Creator: Shum, Hubert 1
1 Durham University, UK
Funder
Innovate UK (partial)
Research methods
Data collection performed in the UK, under Durham University Ethics Approval Ref: COMP-2021-10-03T23-38-21-qhww73
Other description
Keyword
Subject
Artificial intelligence
Location
Durham, UK
Language
Cited in
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications (L. Li, K.N. Ismail, H.P.H. Shum, T.P. Breckon), In Proc. Int. Conf. on 3D Vision, IEEE, pp. 1227-1237, 2021.
https://dro.dur.ac.uk/34293/
Identifier
ark:/32150/r3td96k248j
DurLAR
Rights
Creative Commons Attribution 4.0 International (CC BY)

Publisher
Durham University
Date Created
November, 2021

File Details

Depositor
T. Breckon
Date Uploaded
Date Modified
12 February 2022, 18:02:20
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User L. Li has updated DurLAR : A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications : DurLAR_20211209_S_CAM [dataset] 6 months ago
User L. Li has updated DurLAR_20211209_S_CAM.zip 6 months ago