A Survey of RANSAC enhancements for Plane Detection in 3D Point Clouds

Document Type : Original Article

Authors

Dept. of Computer Science and Eng., Faculty of Elect., Eng., Menoufia University, Menouf, Egypt.

Abstract

Planar surfaces are distinguished features of man-made environment, which are used in many computer vision applications such as object detection, motion segmentation, 3D scene reconstruction, and 3D mapping. One of the most used technique for robust plane detection is the RANdom SAmple Consensus (RANSAC), which is a global iterative method for estimating the parameters of a certain model from input data points contaminated by a set of outliers (noisy data). Unfortunately, the standard RANSAC suffers from some problems regarding the processing time, accuracy of fitting data, and finding an optimal solution. This paper gives a review study of the most recent RANSAC enhancements techniques. In addition, it covers the solving techniques for the speed, accuracy and optimality problems.

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