This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on scene complexity. Few hierarchical and adaptive methods have been proposed to address challenge balancing model fidelity with size. Instead, state-of-the-art mapping approaches require tuning parameters ...

The majority of finite mixture models suffer from not allowing asymmetric tail dependencies within components and capturing non-elliptical clusters in clustering applications. Since vine copulas are very flexible these types dependencies, we propose a novel copula model for continuous data. We discuss the selection parameter estimation problems further formulate new model-based algorithm. use a...