BIM Feature Extraction Dataset

Evaluation


The BIM feature extraction dataset contains data from three indoor scenes with various complexity. For each of the scenes, raw data (point cloud in LAS format) and corresponding BIM line framework (in OBJ format) are provided. Users can evaluate their methods using the downloaded reference line frameworks.
Evaluation by submitting will open for further performance comparison. The evaluation results will be listed on the webpage.


Data Description



Name Size Data description Visualization Line framework
mimap bim 0014.2 MBA closed-loop corridor
mimap bim 01105 MBA corridor and multiple rooms
mimap bim 02237 MBA closed-loop corridor and multiple rooms

Download



mimap_bim_00.zip( 14.2 MB )    [Google]  [Baidu]

mimap_bim_01.zip( 105 MB )    [Google]  [Baidu]

mimap_bim_02.zip( 237 MB )    [Google]  [Baidu]

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unknown
942
United States
405
France
186
Germany
186
China
72
"United States
63
"Germany
60
Singapore
47
"Singapore
45
Cyprus
39
Israel
30
"Cyprus
24
"Canada
17
"China
15
Canada
12
Finland
11
South Korea
9
"France
8
"Russia
7
Ukraine
6
Hong Kong SAR China
6
"Netherlands
4
Ethiopia
3
South Africa
2
Italy
2
Taiwan
1
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1
Turkey
1

Copyright


The MiMAP 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 MiMAP benchmark, please cite both the following papers:

  • C. Wen, Y. Dai, Y. Xia, Y. Lian, C. Wang, J. Li, Towards Efficient 3-D Colored Mapping in GPS/GNSS-denied Environments, IEEE Geoscience and Remote Sensing Letters, 17, 147-151, 2020.

  • 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.