Multitask Multiscale Feature Selection for Point Cloud Registration

Abstract

3-D point cloud registration is a process of solving the geometric transformation between two point clouds. This process is an important issue in computer vision and pattern recognition. The registration methods based on geometric features are highly sensitive to the scale of feature extraction. Changes in scale can introduce inaccuracies in feature descriptions, thereby compromising the reliability of the registration results. To mitigate the impact of feature scale on the outcomes and the high-dimensional issue arising from features of different scales, we propose a method for multiscale point cloud feature selection (FS). We solve the high-dimensional problem of FS by designing a multitask framework. By designing a mutual information dimensionality reduction method, we decomposed the high-dimensional FS task of different descriptors with multiscale features into multiple related low-dimensional FS tasks. Then, by means of the knowledge transfer among these low-dimensional FS tasks, we sought the best feature subset to obtain more robust feature information. We evaluate the effectiveness of our method by conducting extensive experiments on various datasets. The experimental results show that the method outperforms other feature descriptors in terms of descriptive power and robustness and improves the effectiveness of point cloud registration.