Lavva Irina, Hameiri Eyal, Shimshoni Ilan
Department of Computer Science, The Technion-Israel Institute of Technology, Haifa 32000, Israel.
IEEE Trans Syst Man Cybern B Cybern. 2008 Jun;38(3):826-45. doi: 10.1109/TSMCB.2008.918567.
We present a method for the recovery of partially occluded 3-D geometric primitives from range images which might also include nonprimitive objects. The method uses a technique for estimating the principal curvatures and Darboux frame from range images. After estimating the principal curvatures and the Darboux frames from the entire scene, a search for the known patterns of these features in geometric primitives is performed. If a specific pattern is identified, then the presence of the corresponding primitive is confirmed by using these local features. The features are also used to recover the primitive's characteristics. The suggested application is very efficient since it combines the segmentation, classification, and fitting processes, which are part of any recovery process, in a single process, which advances monotonously through the recovery procedure. We view the problem as a robust statistics problem, and we therefore use techniques from that field. A mean-shift-based algorithm is used for the robust estimation of shape parameters, such as recognizing which types of shapes in the scene exist and, after that, full recovery of planes, spheres, and cylinders. A random-sample-consensus-based algorithm is used for robust model estimation for the more complex primitives, such as cones and tori. As a result of these algorithms, a set of proposed primitives is found. This set contains superfluous models which cannot be detected at this stage. To deal with this problem, a minimum-description-length method has been developed, which selects a subset of models that best describes the scene. The method has been tested on series of real complex cluttered scenes, yielding accurate and robust recoveries of primitives.
我们提出了一种从距离图像中恢复部分遮挡的三维几何基元的方法，这些距离图像可能还包含非基元物体。该方法使用一种从距离图像估计主曲率和达布标架的技术。在估计了整个场景的主曲率和达布标架之后，对几何基元中这些特征的已知模式进行搜索。如果识别出特定模式，则通过使用这些局部特征来确认相应基元的存在。这些特征还用于恢复基元的特征。所建议的应用非常高效，因为它将分割、分类和拟合过程（这些是任何恢复过程的一部分）组合在一个单一过程中，该过程在恢复过程中单调推进。我们将该问题视为一个稳健统计问题，因此使用该领域的技术。基于均值漂移的算法用于形状参数的稳健估计，例如识别场景中存在哪些类型的形状，然后全面恢复平面、球体和圆柱体。基于随机抽样一致性的算法用于更复杂基元（如圆锥体和圆环面）的稳健模型估计。由于这些算法，找到了一组提议的基元。这组基元包含在此阶段无法检测到的多余模型。为了解决这个问题，开发了一种最小描述长度方法，该方法选择最能描述场景的模型子集。该方法已在一系列真实复杂的杂乱场景上进行了测试，能够准确且稳健地恢复基元。
