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MD5 file checksums for DurLAR research data collecton 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.

Descriptions

Resource type
Other
Contributors
Creator: Breckon, Toby P. 1
Editor: 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
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
Computer Science
Location
Durham, UK
Language
English
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/r1ws859f701
DurLAR
Rights
Creative Commons Attribution 4.0 International (CC BY)

Publisher
Durham University
Date Created
2/11/21

File Details

Depositor
N. Syrotiuk
Date Uploaded
Date Modified
2 December 2021, 16:12:41
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File format: plain (Plain text)
Mime type: text/plain
File size: 549
Filename: md5sums.txt
Original checksum: 6625ec713868a0266df367ac9c4c58a1
Well formed: true
Valid: true
Character set: US-ASCII
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User N. Syrotiuk has updated MD5 file checksums for DurLAR research data collecton about 2 months ago
User N. Syrotiuk has updated Md5sums for DurLAR research about 2 months ago
User N. Syrotiuk has updated Md5sums for DurLAR research about 2 months ago
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