IEEE Trans Cybern. 2015 Feb;45(2):266-78. doi: 10.1109/TCYB.2014.2324815. Epub 2014 Jun 2.
We propose a new approach for the segmentation of 3-D point clouds into geometric surfaces using adaptive surface models. Starting from an initial configuration, the algorithm converges to a stable segmentation through a new iterative split-and-merge procedure, which includes an adaptive mechanism for the creation and removal of segments. This allows the segmentation to adjust to changing input data along the movie, leading to stable, temporally coherent, and traceable segments. We tested the method on a large variety of data acquired with different range imaging devices, including a structured-light sensor and a time-of-flight camera, and successfully segmented the videos into surface segments. We further demonstrated the feasibility of the approach using quantitative evaluations based on ground-truth data.
我们提出了一种新的方法，用于使用自适应曲面模型将 3-D 点云分割成几何曲面。从初始配置开始，该算法通过新的迭代分裂-合并过程收敛到稳定的分割，其中包括用于创建和删除段的自适应机制。这允许分割根据沿电影的输入数据进行调整，从而产生稳定、时间一致且可跟踪的段。我们使用不同的距离成像设备（包括结构光传感器和飞行时间相机）获取的大量数据对该方法进行了测试，并成功地将视频分割成曲面段。我们进一步使用基于地面真实数据的定量评估证明了该方法的可行性。
