Multi-scale free-form 3D object recognition using 3D models

Abstract

The recognition of free-form 3D objects using 3D models under different viewing conditions based on the geometric hashing algorithm and global verification is presented. The matching stage of the algorithm uses the hash-table prepared in the off-line stage. Given a scene of feature points, one tries to match the measurements taken at scene points to those memorised in the hash-table. The technique used for feature recovery is the generalisation of the CSS method (IEEE Trans. Pattern Anal. Mach. Intell., 14 (1992) 789–805), which is a powerful shape descriptor expected to be an MPEG-7 standard. Smoothing is used to remove noise and reduce the number of feature points to add to the efficiency and robustness of the system. The local maxima of Gaussian and mean curvatures are selected as feature points. Furthermore, the torsion maxima of the zero-crossing contours of Gaussian and mean curvatures are also selected as feature points. Recognition results are demonstrated for rotated and scaled as well as partially occluded objects. In order to verify match, 3D translation, rotation and scaling parameters are used for verification and results indicate that our technique is invariant to those transformations. Our technique for smoothing and feature extraction is more suitable than level set methods or volumetric diffusion for object recognition applications since it is applicable to incomplete surface data that arise during occlusion. It is also more efficient and allows for accurate estimation of curvature values.