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标题:Modeling Urban Scenes From Pointclouds William
作者:William Nguatem, Helmut Mayer
来源:International Conference on Computer Vision (ICCV 2017)
编译:陈世浪
审核:颜青松
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摘要
在本文中,作者提出了一种从点云数据中建模城市场景的MUSP算法。同现有的方法相比,该方法不做曼哈顿世界假设、使用多面体建模和使用NURBS拟合非平地面,MUSP算法鲁棒性高、可拓展性强并且提供了更完整的描述。
首先,作者在无参贝叶斯框架内使用分治算法将场景分割成一致的小块。这些小块通常是有意义的结构,如地面、墙面、屋顶或者屋顶上部结构。作者使用多边形扫描来拟合建筑物和地面的预定义模板,拟合出的NURBS表面应该均匀向前在其中。最后,作者将布尔运算应用到建筑物的多边形、建筑物零件和镶嵌的地面上,以裁剪不必要的几何形状(例如,不平地面的凸出物),从而得到最终的模型。
场景分割的显式贝叶斯公式使作者的方法适用于具有不同噪声、异常和点密度的数据集。作者从图像匹配和激光雷达两方面论证了MISP在3D点云上的鲁棒性。
Abstract
We present a method for Modeling Urban Scenes from Pointclouds (MUSP). In contrast to existing approaches, MUSP is robust, scalable and provides a more complete description by not making a Manhattan-World assumption and modeling both buildings (with polyhedra) as well as the non-planar ground (using NURBS).
First, we segment the scene into consistent patches using a divide-and-conquer based algorithm within a nonparametric Bayesian frame- work (stick-breaking construction). These patches often correspond to meaningful structures, such as the ground, facades, roofs and roof superstructures. We use polygon sweeping to fit predefined templates for buildings, and for the ground, a NURBS surface is fit and uniformly tessellated. Finally, we apply boolean operations to the polygons for buildings, buildings parts and the tesselated ground to clip unnecessary geometry (e.g., facades protrusions below the non-planar ground), leading to the final model.
The explicit Bayesian formulation ofscene segmentation makes our approach suitable for challenging datasets with varying amounts of noise, outliers, and point density. We demon- strate the robustness ofMUSP on 3D pointclouds from im- age matching as well as LiDAR.
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