DurLAR : A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications : DurLAR_20211012_M_LIDAR [dataset]

Breckon, Toby P. (Durham University, UK); Li, Li (Durham University, UK); Ismail, Khalid (Durham University, UK); Shum, Hubert (Durham University, UK)

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

DurLAR : A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications : DurLAR_20211012_M_LIDAR [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
Editor: Li, Li ¹
Creator: 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/r3m900nt40h
DurLAR
Rights: Creative Commons Attribution 4.0 International (CC BY)
Publisher: Durham University
Date Created: November, 2021

File Details

Depositor: T. Breckon
Date Uploaded: 8 February 2022, 15:02:20
Date Modified: 10 February 2022, 08:02:51
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User N. Syrotiuk has updated DurLAR : A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications : DurLAR_20211012_M_LIDAR [dataset]	almost 3 years ago
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User N. Syrotiuk has updated DurLAR_20211012_M_LIDAR.zip	almost 3 years ago
User N. Syrotiuk has updated DurLAR : A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications : DurLAR_20211012_M_LIDAR [dataset]	almost 3 years ago