Outlier detection for scanned point clouds using majority voting. (May 2015)

• Record Type: Journal Article
• Authors: Wang, Yutao; Feng, Hsi-Yung
• Is Part Of: Computer aided design. Volume 62(2015)
• Page Start: 31
• Page End: 43
• Publication Date: 2015-05
• Subjects: 3D Laser scanning -- Point cloud -- Outlier detection -- Non-isolated outliers -- Majority voting -- Feature preservation; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2014.11.004
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: When scanning an object using a 3D laser scanner, the collected scanned point cloud is usually contaminated by numerous measurement outliers. These outliers can be sparse outliers, isolated or non-isolated outlier clusters. The non-isolated outlier clusters pose a great challenge to the development of an automatic outlier detection method since such outliers are attached to the scanned data points from the object surface and difficult to be distinguished from these valid surface measurement points. This paper presents an effective outlier detection method based on the principle of majority voting. The method is able to detect non-isolated outlier clusters as well as the other types of outliers in a scanned point cloud. The key component is a majority voting scheme that can cut the connection between non-isolated outlier clusters and the scanned surface so that non-isolated outliers become isolated. An expandable boundary criterion is also proposed to remove isolated outliers and preserve valid point clusters more reliably than a simple cluster size threshold. The effectiveness of the proposed method has been validated by comparing with several existing methods using a variety of scanned point clouds. Highlights: A robust method to detect and remove all types of outliers in scanned point clouds. Ability to preserve valid point clusters of small size. Effectiveness validated with a variety of scanned point clouds.