3D modeling using hierarchical feature point and spatiotemporal relationship This paper proposes a new 3D modeling technique based on feature points using spatiotemporal relationship. Normally, the generation of a 3D model from a real scene requires the computation of the depth of the model vertices from a dense correspondence map of the whole image. This is very time consuming, plus it is also quite difficult to achieve an accurate depth. The proposed method can generate a 3D model of an object based on identifying the correspondence of certain feature points without the need for a dense correspondence map. The proposed method consists of three parts: the extraction of an object, the extraction of feature points, and hierarchical 3D modeling using the classified feature points. This method is effective in generating a 3D model and expressing the smoothness of plain regions and sharpness of edges.

3D Feature Matching for Point Cloud Object Extraction Effective object extraction plays an important role in many point cloudbased applications. This letter proposes a 3D feature matching framework for point cloud object extraction. To determine the optimal affine transformation parameters for each template feature point, a convex dissimilarity function and the locally affineinvariant geometric constraints are designed to construct the overall objective function. The 3D feature matching framework is integrated into a point cloud object extraction workflow. Extraction results on six test data sets show that average completeness, correctness, quality, and    F_1   measure of 0.96, 0.97, 0.93, and 0.96, respectively, are obtained in extracting light poles, vehicles, and palm trees. Comparative studies also confirm that the proposed method performs effectively and robustly, and exhibits superior or compatible performance over the other compared methods.