3D object recognition using scale-invariant features

As 3D scanning technology develops, it becomes easier to acquire various 3D surface data; thus, there is a growing need for 3D data registration and recognition technology. Many existing studies use local descriptors using local surface patches, and most of them use a fixed support radius, so they cannot cope perfectly when the model and scene have different scales. In this study, we propose a perfectly scale-invariant feature selection algorithm by extending the 2D SIFT algorithm (Lowe in Int J Comput Vis 60(2):91–110, 2004) to a 3D mesh. The feature selection method proposed in this study can obtain highly repeatable feature points and support radii regardless of mesh scale. The selected features can effectively describe the local information by the new shape descriptor proposed in this study. Unlike existing shape descriptors, it is possible to perform scale-invariant 3D object recognition and achieve a high recognition rate when combined with the feature point selection algorithm proposed in this study by using the gradients of the scalar functions defined on the 3D surface. We also reduced the searching space and lowered the false positive rate by suggesting a new RANSAC-based transformation hypotheses generation algorithm. Our 3D object recognition algorithm achieves recognition rates of 100 and 98.5%, respectively, when tested on the U3OR and CFVD datasets, exceeding the results of previous studies.