Feature selection for quality assessment of indoor mobile mapping point clouds

Fangfang Huang; Chenglu Wen; Cheng Wang; Jonathan Li

doi:10.1117/12.2234945

2 March 2016 Feature selection for quality assessment of indoor mobile mapping point clouds

Fangfang Huang, Chenglu Wen, Cheng Wang, Jonathan Li

Proceedings Volume 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015); 99010B (2016) https://doi.org/10.1117/12.2234945
Event: 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 2015, Xiamen, China

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.

Citation Download Citation

Fangfang Huang, Chenglu Wen, Cheng Wang, and Jonathan Li "Feature selection for quality assessment of indoor mobile mapping point clouds", Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010B (2 March 2016); https://doi.org/10.1117/12.2234945

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available