Dealing with Noisy Data for the Generation of Point Cloud Skeletons

• Main Authors: Lin, Yi Peng, 林逸芃
• Other Authors: Hsu, Kuo Wei
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/51153154468241508657

碩士 === 國立政治大學 === 資訊科學學系 === 102 === The skeleton of a visual object or a 3D model is a representation that can reveal the topological structure of the object or the model, and therefore it can be used in various applications such as shape analysis and computer animation. Over the years there have been many studies working on the extraction of the skeleton of an object. However, most of those studies focused on complete and clean data (even though some of them took missing values into account), while in practice we often have to deal with incomplete and unclean data, just as there might be missing values and noise in data. In this thesis, we study noise handling, and we put our focus on preprocessing a noisy point cloud for the generation of the skeleton of the corresponding object. In the proposed approach, we first identify data points that might be noise and then lower the impact of the noisy values. For identifying noise, we use supervised learning on data whose features are density and distance. For lowering the impact of the noisy values, we use triangular surfaces and projection. The preprocessing method is flexible, because it can be used with any tool that can extract skeletons from point clouds. We conduct experiments with several 3D models and various settings, and the results show the effectiveness of the proposed preprocessing approach. Compared with the unprocessed model (which is the original model with the added noise), if we apply the proposed preprocessing approach to a noisy point cloud before using a tool to generate the skeleton, we can obtain a skeleton that contains more topological characteristics of the model. Our contributions are as follows: First, we show how machine learning can help computer graphics. Second, we propose to use distance and density as features in learning for noise identification. Third, we propose to use triangular surfaces and projection to save execution time in noise reduction. Fourth, the proposed approach could be used to improve 3D scanning.