Indoor Laser Scanning Dataset


  • Indoor laser scanning dataset provides fours indoor point clouds data based on SLAM-mapping process. The scenes include large scale indoor parking lots, corridor and multiple rooms.


  • This dataset also includes line framework extraction results of the scenes and provides a brief description of the indoor scene.


Data Description


Name Number of Points Scene Visualization Line framework
Scene 17.90 millionIndoor parking area
Scene 23.85 millionIndoor parking area
Scene 32.10 millionCorridor
Scene 48.62 millionMultiple rooms

Download



Scene 1. 7z ( 0.255 GB ) line_Scene 1. 7z( 56 KB )

Scene 2. 7z ( 0.353 GB ) line_Scene 2. 7z( 31 KB )

Scene 3. 7z ( 0.066 GB ) line_Scene 3. 7z( 20 KB )

Scene 4. 7z ( 0.304 GB ) line_Scene 4. 7z( 17 KB )

Download per country

United States
875
Canada
657
France
396
China
381
Germany
185
unknown
114
Netherlands
78
"Canada
53
Singapore
50
Israel
48
"Germany
40
"United States
38
Cyprus
37
"Singapore
29
"Netherlands
27
South Korea
27
Japan
24
"Finland
17
Russia
16
Hong Kong SAR China
12
Turkey
12
"Cyprus
11
Finland
7
Australia
7
India
6
"China
5
Italy
5
Austria
4
Spain
4
Iran
4
Belgium
3
ul
3
United Kingdom
2
Switzerland
2
Colombia
2
Indonesia
2
South Africa
1
Thailand
1
Taiwan
1
Luxembourg
1

Copyright


The Indoor laser scanning dataset is published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License (https://creativecommons.org/licenses/by-nc-sa/3.0/). You must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. Contact us if you are interested in commercial usage.


Citation


If you use this dataset please cite the following paper:

  • C. Wang, S. Hou, C. Wen, Z. Gong, Q. Li, X. Sun, J. Li, Semantic Line Framework-based Indoor Building Modeling using Backpacked Laser Scanning Point Cloud, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 143, pp. 150-166, 2018.