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Fangfang Huang

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Proceedings Article | 2 March 2016 Paper

Feature selection for quality assessment of indoor mobile mapping point clouds

Fangfang Huang, Chenglu Wen, Cheng Wang, Jonathan Li

Proceedings Volume 9901, 99010B (2016) https://doi.org/10.1117/12.2234945

KEYWORDS: Clouds, Feature selection, Feature extraction, Cameras, Principal component analysis, Associative arrays, Data modeling, 3D modeling, Data processing, Data acquisition

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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.

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