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For example, due to the low resolution of a camera, we need to fuse more frames into a global scene. It is critically important to capture these point clouds in such a way because (1) the Field of View (FoV) of a depth camera usually is not big enough to capture the massive scene, such as the forest and city, and (2) the quality of a single-achieved point cloud is generally not acceptable. Therefore, we usually move the 3D acquisition device around a target object to finish a throughout scan, such that retrieving multiple point clouds at different viewpoints for better rebuilding the 3D environment or recovering the 3D shape of an object. However, it is impossible to obtain all the point cloud data of an object or a scene at once in practice.
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Therefore, 3D point clouds have been widely produced and processed to provide accurate and fast descriptions of the 3D geometries of objects in various applications, such as 3D model reconstruction, geometry quality inspection, and robotic manipulation. Specifically, the point cloud can be used to measure the distance between objects in the current frame, achieve an accurate representation of the shape of an object, and so on. In contrast to a 2D image, a 3D point cloud data contain much more information of an object or a scene, and hence, it can provide a better understanding and description of the real world.
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With the advent of low-cost three-dimensional (3D) imaging devices and the development of professional 3D acquisition techniques, more and more research efforts have been attracted toward the 3D point cloud from industry to academia in recent years.
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The goal is to help readers quickly get into the problems of their interests related to point could registration and to provide them with insights and guidance in finding out appropriate strategies and solutions. This review attempts to serve as a tutorial to academic researchers and engineers outside this field and to promote discussion of a unified vision of point cloud registration. In this survey paper, we present the overview and basic principles, give systematical classification and comparison of various methods, and address existing technical problems in point cloud registration. In this revolution, the most challenging but imperative process is point could registration, i.e., obtaining a spatial transformation that aligns and matches two point clouds acquired in two different coordinates. A point cloud as a collection of points is poised to bring about a revolution in acquiring and generating three-dimensional (3D) surface information of an object in 3D reconstruction, industrial inspection, and robotic manipulation.