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]: Proposed is a robust algorithm for scaling registration of point sets, in the way of introducing the bidirectional distance measurement into the least square problem, which is solved in the way the iterative closest point algorithm does by iterations. At each iterative step of this algorithm, bidirectional correspondence is set up between two m-dimensional point sets, and then a simple and fast iterative algorithm is used to compute the rotation, scale and translation transformations. Experimental results demonstrate its robustness and high accuracy over previous algorithms.
Our work is based on SfM approach and the use of the SICP algorithm [1] to register both point clouds with the only constraint being to locate them relatively close to each other.