An approximate and efficient method for optimal rotation alignment of 3D models.
In many shape analysis applications, the ability to find the best rotation that aligns two models is an essential first step in the analysis process. In the past, methods for model alignment have either used normalization techniques, such as PCA alignment, or have performed an exhaustive search over the space of rotation to find the best optimal alignment. While normalization techniques have the advantage of efficiency, providing a quick method for registering two shapes, they are often imprecise and can give rise to poor alignments. Conversely, exhaustive search is guaranteed to provide the correct answer, but, even using efficient signal processing techniques, this type of approach can be prohibitively slow. In this paper, we present a new method for aligning two 3D shapes. We show that the method is markedly faster than existing approaches based on efficient signal processing and we provide registration results demonstrating that the alignments obtained using our method have a high degree of precision and are markedly better than those obtained using normalization.
Kazhdan
Michael
M
Johns Hopkins University, Baltimore, MD 21218, USA. misha@cs.jhu.edu
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012399:Rotation; D012680:Sensitivity and Specificity; D013382:Subtraction Technique