Shen Yang
ABSTRACT
This paper presents a rapid and robust method to align large sets of range scans captured by a 3D scanner automatically. The method incorporates the color information from the range data into the pairwise registration. Firstly, it detects the features using SIFT (Scale-Invariant Feature Transform) on grayscale images generated from two range scans to align. Then a quasi-dense matching algorithm, based on the match propagation principle, is applied to specify the matching pixel pairs between two images. All matches obtained are mapped to 3D space but in different world coordinates, and fitered by the 3D geometry constraint discovered from the range data. The remaining set of point correspondences is used to estimate the rigid transformation. Finally, a modified ICP (Iterative Closest Point) algorithm is applied to refine the result. The paper also describes a framework to use this alignment method for object reconstruction. The reconstruction proceeds by acquiring several range scans with color information from different directions, following which pair-wise of range data are aligned with the above method selectively and iteratively. Then a model graph containing the correct pair-wise matches is created and a span tree specifying a complete model is constructed. Finally a global optimization is performed to refine the result. This reconstruction technique achieves a robust and high performance in the application of rebuilding the 3D models of culture heritages for virtual museum automatically.
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Index Terms
- A novel method based on color information for scanned data alignment
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