In this work, we improved the efficiency and accuracy of our proposed cylinder-based registration and model fitting method of point clouds by terrestrial laser scanners for the as-built modeling of piping systems. Our algorithm simultaneously calculated the scanner parameters and cylinder parameters to avoid the propagation of registration errors and modeling errors. Coarse registration is performed by finding the alignment of the cylinder axes based on the random sample consensus approach and using a hash table. The efficiency of the coarse registration is improved by introducing a three-dimensional hash table. The fine registration and modeling is performed by minimizing the fitting errors of the cylinders as a nonlinear function of the positional and geometric parameters of scanners and cylinders. An iteratively reweighted least squares method is applied to the fine registration and modeling, leading to improved robustness. Moreover, for the modeling of pipes that are slightly bent due to gravity, incident angle filtering of scanned points and cylinder subdivision of the pipes to be modeled are introduced. The efficiency and robustness of the improved algorithm were compared with the previous approach using both artificial and real point clouds. The effectiveness of incident angle filtering and cylinder subdivision was confirmed. The proposed algorithm achieved the level of cylindrical modeling precision required for the renovation work of piping systems.

Spinal deformity is a disease that causes a three-dimensional deformation of the spinal column. When it worsens, surgery is required to screw correction rods to the spinal column. However, the surgery requires intraoperative rod bending work, which burdens the patients and causes unexpected rod breakage inside the body. Therefore, "pre-bent" rods comprising several rods with standardized shapes have been proposed to solve these problems. When designing pre-bent rods, knowing the number of rods to be prepared and the kinds of shapes required is essential. In this paper, we propose a geometric processing technique to identify an optimal set of these standardized pre-bent rod shapes for surgeries on adult spinal deformity and describe the similarity evaluation among existing rod shapes using CT scan, medial axis extraction, and iterative closest point algorithm. Moreover, we present the derivation of standardized rod shapes using hierarchical cluster analysis and the best fit of the B-spline curve to each cluster. Finally, we discuss the effectiveness of prebent rod shapes derived from CT scans of 26 existing rods of 13 patients.