You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A review of recent range image registration methods with accuracy evaluation 
Chapter Title:  Introduction 
1. Abstract of Cited Paper (BIB001):  A family of high-performance 3D imaging laser sensors are presented providing rapid acquisition of 3D data of naturally reflecting objects within a wide field-of-view and with ranges up to 1000 meters. Beside the basic 3D-information the sensors also supply, pixel for pixel, true color information gained by an additional passive channel. This color information allows an instantaneous automatic texturing of 3D models. The sensors are of a rugged and compact design and can thus be used even under adverse environmental conditions. Potential applications include scene acquisition for virtual reality modeling, 3D-imaging of large buildings in the field of architecture, topographic mapping and dimensional measurements of vessels, furnaces, tanks, etc. The sensor makes use of high-speed laser ranging based on the time-of-flight method using a pulsed laser source in combination with precise mechanical scanning of the measuring beam. Data are provided at a standard parallel interface allowing easy integration with any standard PC based system for data visualization and data processing. These data are provided with an average data rate up to 18000 measurements per second. Examples of data acquired with the sensors are presented, accuracy and ranging capability are discussed. 
2. Abstract of Cited Paper (BIB002):  Coded structured light is considered one of the most reliable techniques for recovering the surface of objects. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. The decoded points can be triangulated and 3D information is obtained. We present an overview of the existing techniques, as well as a new and definitive classification of patterns for structured light sensors. We have implemented a set of representative techniques in this field and present some comparative results. The advantages and constraints of the different patterns are also discussed. 
3. Abstract of Cited Paper (BIB003):  A heuristic method has been developed for registering two sets of 3-D curves obtained by using an edge-based stereo system, or two dense 3-D maps obtained by using a correlation-based stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision, which is required for environment modeling (e.g., building a Digital Elevation Map). Objects are represented by a set of 3-D points, which are considered as the samples of a surface. No constraint is imposed on the form of the objects. The proposed algorithm is based on iteratively matching points in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subset-subset matching. A least-squares technique is used to estimate 3-D motion from the point correspondences, which reduces the average distance between points in the two sets. Both synthetic and real data have been used to test the algorithm, and the results show that it is efficient and robust, and yields an accurate motion estimate.

Literature Review:
Surface registration is an intermediate but crucial step inside the whole computer vision complexity. Registration benefits by previous steps such as basically surface reconstruction and view pose estimation without ignoring image processing and camera calibration, and it is the input for further steps such as object model computation. The variety of applications is worthwhile: reverse engineering and mould fabrication in the manufacturing process, artifact reproduction and 3D modeling of carving pieces and sculptures both with applications in the souvenir industry and virtual museums, and many others such as augmented reality in graphics and map building in robotics. There are many sorts of range finders involved in surface reconstruction which are based on time-of-flight lasers BIB002 , laser scanning  , stereovision  , and pattern projection BIB003 . All of these range finders have their pros and cons and different fields of applications but all of them provide a more or less accurate reconstruction of a surface. In all the cases, the reconstruction does not represent the entire object due to occlusions and the limited field of view of the sensor. In order to solve this problem a set of range images taken from different positions are acquired and then must be fused. Many devices to overcome this task exist in the market, which are basically based on calibrated mechanics to compute the geometry between the views such as rotating tables and robot arms. However, these devices are still limited due to the fact that the object must be located inside the device or close to the working area. Many applications involved with large surfaces or simply with the complexity of moving the object to the scanning system demand a process of accurate registration without a previous knowledge of the pose of the views. This is the aim of this paper, which presents a comprehensive survey of surface registration including a new classification, the description of every category and comparative experimental results with both synthetic and real data. Other interesting overviews are already available in the literature. For instance, Rusinkiewicz and Levoy  published a paper involved on comparing the ICP techniques specially centered in the point-to-point and point-to-plane correspondences and different methods involved in sampling the control points. Although some coarse registration techniques are explained, the paper is incomplete because multi-view registration is neglected. Furthermore, only synthetic data is used in comparing the different techniques without bringing in some of the problems involved using real images in the tests and all the views used in the experiments have overlapping regions which easies the registration. Besides, Dalley and Flynn  presented another study centered in pair-wise registration and especially in the different techniques involved in removing the outliers such as basically the Schtz's distance threshold BIB001 and the Zhang's statistical outlier BIB004 . Our paper is a completing survey and an extension of these previous works because it analyses the different techniques in both pair-wise and multi-view registration and contributes with experimental results with real range images. The remainder is organized as follows. First, a new classification is presented in Section 2. Second, coarse registration techniques are presented and compared in Section 3, while fine registration is discussed in Section 4. Next, experimental results compare the surveyed techniques in the presence of both synthetic data and real data acquired some of them from real scanners and others provided by other authors. The article ends with the conclusions, which includes a discussion of the advantages and drawbacks of every group of techniques sort out in the new classification proposal.