3D object recognition by fast spherical correlation between combined view EGIs and PFT

This paper proposes a method to recognize 3D object from probed range image under arbitrary pose by fast spherical correlation. First, all view EGIs under different viewpoints are extracted and combined onto a Gaussian sphere to form a feature description for each object. Second, the probed range image at arbitrary pose is represented as a PFT feature by phase-encoded Fourier transform and the PFT feature is mapped onto the Gaussian hemisphere by coordinates transforms and intensity scaling. Third, the spherical correlation algorithm based on spherical harmonic functions is used to do matching and similarity measurement between mapped PFT and combined view EGIs. The spherical correlation peak can output both of the recognition result and pose estimation. The experimental results proved that the proposed method can not only recognize totally different objects but also has enough discriminating capability for scalable dataset of similar objects.