| Summary: | 碩士 === 國立交通大學 === 資訊學院資訊學程 === 101 === In this thesis, the artificial potential field is used as the foundation for matching point cloud with 3D vehicular shapes. An initially small input object (point cloud) is placed inside shape models will experience repulsive force and torque arising from the potential field. A better match in shape between the shape model and the input object can be obtained if the input object translates and reorients itself to reduce the potential while growing in size. The shape model which allows the maximum growth of the input object corresponds to the best match and thus represents the shape of the input object. Since the point cloud does not cover the complete 3D object, the best match cannot be determined only by the potential model. Therefore, the research of this thesis also considers the minimum bounding boxes of projections of the point cloud and the distribution of objects points in the z-direction to improve the matching results. Based on the above concept, three methods are considered to refine the shape matching results via silhouette minimization, hood finding and roof finding. Experiment results show that the methods proposed in this thesis can indeed increase the rate of recognition of real vehicular shapes.
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