You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this paper we present a semi-automatic 2D-3D local registration pipeline capable of coloring 3D models obtained from 3D scanners by using uncalibrated images. The proposed pipeline exploits the Structure from Motion (SfM) technique in order to reconstruct a sparse representation of the 3D object and obtain the camera parameters from image feature matches. We then coarsely register the reconstructed 3D model to the scanned one through the Scale Iterative Closest Point (SICP) algorithm. SICP provides the global scale, rotation and translation parameters, using minimal manual user intervention. In the final processing stage, a local registration refinement algorithm optimizes the color projection of the aligned photos on the 3D object removing the blurring ghosting artefacts introduced due to small inaccuracies during the registration. The proposed pipeline is capable of handling real world cases with a range of characteristics from objects with low level geometric features to complex ones.

Cited Abstract(s):
[1]: The photorealistic modeling of large-scale scenes, such as urban structures, requires a fusion of range sensing technology and traditional digital photography. This paper presents a system that integrates multiview geometry and automated 3D registration techniques for texture mapping 2D images onto 3D range data. The 3D range scans and the 2D photographs are respectively used to generate a pair of 3D models of the scene. The first model consists of a dense 3D point cloud, produced by using a 3D-to-3D registration method that matches 3D lines in the range images. The second model consists of a sparse 3D point cloud, produced by applying a multiview geometry (structure-from-motion) algorithm directly on a sequence of 2D photographs. This paper introduces a novel algorithm for automatically recovering the rotation, scale, and translation that best aligns the dense and sparse models. This alignment is necessary to enable the photographs to be optimally texture mapped onto the dense model. The contribution of this work is that it merges the benefits of multiview geometry with automated registration of 3D range scans to produce photorealistic models with minimal human interaction. We present results from experiments in large-scale urban scenes.

[2]: The photorealistic acquisition of 3D objects often requires color information from digital photography to be mapped on the acquired geometry, in order to obtain a textured 3D model. This paper presents a novel fully automatic 2D 3D global registration pipeline consisting of several stages that simultaneously register the input image set on the corresponding 3D object. The first stage exploits Structure From Motion (SFM) on the image set in order to generate a sparse point cloud. During the second stage, this point cloud is aligned to the 3D object using an extension of the 4 Point Congruent Set (4PCS) algorithm for the alignment of range maps. The extension accounts for models with different scales and unknown regions of overlap. In the last processing stage a global refinement algorithm based on mutual information optimizes the color projection of the aligned photos on the 3D object, in order to obtain high quality textures. The proposed registration pipeline is general, capable of dealing with small and big objects of any shape, and robust. We present results from six real cases, evaluating the quality of the final colors mapped onto the 3D object. A comparison with a ground truth dataset is also presented.

[3]: This paper presents an overview of our research project on digital preservation of cultural heritage objects and digital restoration of the original appearance of these objects. As an example of these objects, this project focuses on the preservation and restoration of the Great Buddhas. These are relatively large objects existing outdoors and providing various technical challenges. Geometric models of the great Buddhas are digitally achieved through a pipeline, consisting of acquiring data, aligning multiple range images, and merging these images. We have developed two alignment algorithms: a rapid simultaneous algorithm, based on graphics hardware, for quick data checking on site, and a parallel alignment algorithm, based on a PC cluster, for precise adjustment at the university. We have also designed a parallel voxel-based merging algorithm for connecting all aligned range images. On the geometric models created, we aligned texture images acquired from color cameras. We also developed two texture mapping methods. In an attempt to restore the original appearance of historical objects, we have synthesized several buildings and statues using scanned data and a literature survey with advice from experts.

[4]: Texture mapping on scanned objects, that is, the method to map current color images on a 3D geometric model measured by a range sensor, is a key technique of photometric modeling for virtual reality. Usually range and color images are obtained from different viewing positions, through two independent range and color sensors. Thus, in order to map those color images on the geometric model, it is necessary to determine relative relations between these two viewpoints. In this paper, we propose a new calibration method for the texture mapping; the method utilizes reflectance images and iterative pose estimation based on a robust Mestimator. Moreover, since a 2D texture image taken from one viewing point is a partial view of an object, several images must be mapped onto the object in order to cover the entire 3D geometric model. In this paper, we propose the new simultaneous registration technique of several images and geometric model based on 2D-3D edge correspondence and the epipolar constraint between images.

[5]: We present an approach to automatically register a large set of color images to a 3D geometric model. The problem arises from the modeling of real-world environments, where surface geometry is acquired using range scanners whereas the color information is separately acquired using untracked and uncalibrated cameras. Our approach constructs a sparse 3D model from the color images using a multiview geometry technique. We project special light patterns onto the scene surfaces to increase the robustness of the multiview geometry reconstruction. The sparse model is then approximately aligned with the detailed model. Planes found in the detailed model are exploited to refine the registration. Finally, the registered color images are mapped to the detailed model using weighted blending, with careful consideration of occlusion and the preservation of image details.
Nevertheless there are 3D scanners which provide also reflectance images and the registration is performed in a 2D space [3] , [4] . On the other hand, some authors perform their registration in a 3D space reconstructing the 3D object from the 2D images and aligning both 3D objects [1] , [5] , [2] . This procedure is carried out in two steps: (1) 3D reconstruction and (2) point cloud alignment. Through the widely used Structure from Motion technique (SfM), a 3D reconstruction and intrinsic and extrinsic camera parameters are recovered without making any assumptions about the images in the scene. The registration is usually performed by selecting correspondences [5] that minimize the distances between a set of points.