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Proceedings Paper

Feature selection for quality assessment of indoor mobile mapping point clouds
Author(s): Fangfang Huang; Chenglu Wen; Cheng Wang; Jonathan Li
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Paper Abstract

Owing to complexity of indoor environment, such as close range, multi-angle, occlusion, uneven lighting conditions and lack of absolute positioning information, quality assessment of indoor mobile mapping point clouds is a tough and challenging task. It is meaningful to evaluate the features extracted from indoor point clouds prior to further quality assessment. In this paper, we mainly focus on feature extraction depend upon indoor RGB-D camera for the quality assessment of point cloud data, which is proposed for selecting and screening local features, using random forest algorithm to find the optimum feature for the next step’s quality assessment. First, we collect indoor point clouds data and classify them into classes of complete or incomplete. Then, we extract high dimensional features from the input point clouds data. Afterwards, we select discriminative features through random forest. Experimental results on different classes demonstrate the effective and promising performance of the presented method for point clouds quality assessment.

Paper Details

Date Published: 2 March 2016
PDF: 7 pages
Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010B (2 March 2016); doi: 10.1117/12.2234945
Show Author Affiliations
Fangfang Huang, Xiamen Univ. (China)
Chenglu Wen, Xiamen Univ. (China)
Cheng Wang, Xiamen Univ. (China)
Jonathan Li, Xiamen Univ. (China)


Published in SPIE Proceedings Vol. 9901:
2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015)
Cheng Wang; Rongrong Ji; Chenglu Wen, Editor(s)

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