Non-rigid point set registration using color and data downsampling

Nowadays, non-rigid registration problem is an active research topic in computer vision. Various proposals exist which face the problem from different perspectives, but it is still a challenging problem. Currently, with the new low-cost RGB-D sensors, the use of both, color and 3D information, is getting more interest in many applications. In this paper, we present a non-rigid registration technique based on CPD, and including color information along with 3D data, to estimate the non-rigid transformation. As the input data size is critical in the processing time, a sampling technique is required. Five sampling techniques are evaluated: a bilinear sampling, a normal-based, a color-based, a combination of the normal and color-based samplings, and a Growing Neural Gas based approach. All of them have been evaluated with the already presented non-rigid registration methods. Results show the performance of each sampling method, obtaining better results for the registration process using color-based sampling techniques.