# Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud
## 1 Introduction  
A variety of studies have been conducted in the computer vision field to recognize objects in 3D space. For example, researchers have studied depth information through a camera (stereo matching) or an infrared camera that imitates an eye as well as object recognition and movement by measuring depth through a laser scanner. Among them, measurement using a space scanner is relatively more precise than measurement using a camera; thus, it is widely used to construct point clouds. The 3D scanner emits an infrared laser into space and calculates the distance by measuring the time it takes for the object to be reflected back. The light has several characteristics in a specific time and space. Among these, maintaining straightness and consistent velocity enables a relatively accurate distance measurement. Point clouds obtained from 3D scanners are generally sufficient for human perception but are not recognized by computers. Therefore, the computer is subjected to various preprocessing steps, such as normal estimation, segmentation, and filtering, to recognize objects in the point cloud (shown in Figure 1). The normal estimate is obtained by constructing a covariance matrix from the surrounding points using the k-d tree data structure and calculating the eigenvectors. When the normal estimation is completed, it is divided into a set of points with a common normal that meet certain criteria. The divided sets undergo postprocessing to determine what kind of object each set is. In recent years, plant construction has been based on 3D design, so it is easy to check the structure interference or make modifications/changes, and it is possible to efficiently design and to train in areas such as fire, disaster, and operation using virtual technology. On the other hand, it is more efficient to maintain/repair through reverse design because the existing plant site is huge, and reconstruction is impossible. Reverse design is aimed at obtaining meaningful data through digitization in three dimensions for constructed facilities or facilities under construction. Therefore, the demand for reverse design is increasing. This paper describes a method for quickly recognizing cylinder-shaped objects that take up a large portion of plant facilities. A plant facility of large scale entails a large opportunity cost for 3D scanning and consists of a very large set of points. Therefore, since matching requires a lot of computer resources (CPU, memory, db, power usage), time, and manpower, relatively fast and accurate matching is the only way to reduce costs. In general, cylinder-shaped objects are matched using region growing, Hough transform, random sample consensus (RANSAC), and machine learning. Region growing is a process for segmentation and is classified into a set of normals that maintain similar directionality in a point cloud. Hough transform is a method of estimating the direction of a cylinder ultimately by replacing the normal formed by segmented data with a Hough space [2]. Both of these methods result in an exponential increase in processing time and memory usage as the numbers of data points increase. On the other hand, primitive fitting using RANSAC is relatively fast, but gives inaccurate results due to noise and curved areas. To overcome this drawback, this paper uses RANSAC, estimates the direction of the cylinder through sphere fitting, and computes the cylinder length through principal component analysis (PCA). The proposed method is robust against noise and shows fast matching results while maintaining relative accuracy. Because the plant is large (the Ulsan Petrochemical Complex site in Korea has 29,916,000 square meters), it takes a significant amount of opportunity cost to handle reverse engineering manually. In addition, since many computer resources are used for reverse design, appropriate filtering and data structure must be used so that it can be processed without overflow. Therefore, complete automation is virtually impossible.