Study On Point Cloud Processing Based On Kernel Machine Learning Method

In reverse engineering, advances in 3D scanning technologies have promoted the emergence and rapid development of point-based techniques. Points are primitive units of surface modeling and rendering, point-based techniques have become an important research field of reverse engineering. The particular advantages of point-based techniques are the efficiency at reconstructing and rendering very complex objects and environments, capability of dealing with dense scattered point cloud, and simplicity of rendering algorithms. In this paper, some important problems about point cloud, such as optimized sub-sampling curve plotting, hole filling, point cloud modeling, and kernel machine based on statistic learning theory and wavelet, have been analyzed and discussed. Several algorithms based on kernel technology have been proposed. Their feasibility has been shown by numerical experiments. The main contributions of this paper are following:An algorithm of optimized sub-sampling curve plotting has been proposed. Sparse circular or elliptic surface cells with space information can be extracted from dense point cloud by sub-sample. Optimized surface is found by greedy algorithm, and holes are filled by full optimized technology. Theoretical analysis and contrastive experiment results show following advantages of the algorithm: realization of full surface plotting without holes by fewer curves under given tolerance, good performance of smooth surface, well situated sampling density and robust controllability of error.Found on statistic learning theory (SLT), a sub-sampling curve presentation based on support vector machine (SVM) has been put forward. After discussions of surface figure, the lineament of point cloud and its imperceptible features have condtructed by ε-support vector machine and v - support vector machine. Greedy algorithm has been utilized to solve the geometry problems. Experiment results show that a smooth surface with good lubricity can be generated quickly.Based on the analyzing the features of point cloud hole filling technology, a kind of algorithm of hole filling based on kernel machine learning method is put forward. For each pixel of raw figure, color features of eight neighbors are distilled out to generate a group of incomplete curves. The holes on the curves are filled by regression based on kernel learning machine. Contrastive experiments of several algorithms and different kennels have been designed. The experiment results show that the algorithms proposed are satisfactory and could find their importance in...