Point Cloud Filtering via Generalized Gaussian Mixture Model

3D point cloud is extensively utilized to reconstruct the surface of the object. However, in many real-world situations, the acquired point cloud is corrupted by noise due to the insufficient precision of the measuring instruments. To filter the noise, a typical conventional method showing promising results attempts to capture the probabilistic distribution of the data with Gaussian mixture model (GMM). However, the assumption that the data only complies with Gaussian distribution restricts its performance if the data is non-Gaussian. In this paper we propose to filter the noise by generalized Gaussian mixture model (GGMM). With the shape parameter in GGMM determining the type of probabilistic model of each component, GGMM is able to capture the underlying distribution of the point cloud more accurately regardless of the non-Gaussianity of the data, which leads to a better performance in preserving geometric features while filtering the point cloud. Experiment with synthetic noisy data demonstrates the effectiveness of the proposed method both qualitatively and quantitatively.