Efficient feature tracking of time-varying surfaces using multi-scale motion flow propagation

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• 作者：Bin Liao ; Chunxia Xiao ; Zhiqiang Pang
• 关键词：Multi-scale filtering ; Time-varying surfaces ; Geometry feature tracking ; Geometry motion flow ; Gaussian mixture model
• 刊名：Computer-Aided Design
• 出版年：2013
• 出版时间：November, 2013
• 年：2013
• 卷：45
• 期：11
• 页码：1394-1407
• 全文大小：4721 K

文摘

This paper presents a framework for efficient feature tracking of time-varying surfaces. The framework can not only capture the dynamic geometry features on time-varying surfaces, but can also compute the accurate boundaries of the geometry features. The basic idea of the proposed approach is using the multi-scale motion flow and surface matching information to propagate the feature frame on time-varying surfaces. We first define an effective multi-scale geometry motion flow for the time-varying surfaces, which efficiently propagates the geometry features along the time direction of the time-varying surfaces. By combining both the approximately invariant signature vectors and geometry motion flow vectors, we also incorporate the shape matching into the system to process feature tracking for time-varying surfaces in large deformation while with low frame sampling rate. Our approach does not depend on the topological connection of the underlying surfaces. Thus, it can process both mesh-based and point-based time-varying surfaces without vertex-to-vertex correspondence across the frames. Feature tracking results on different kinds of time-varying surfaces illustrate the efficiency and effectiveness of the proposed method.