Breaking the Fog: Defining and Orienting Surfaces in Complex Point Cloud Datasets

We present a vertex clustering algorithm for the purposes of surface determination and normal estimation that can help provide detailed visualizations of complex point cloud datasets. The proposed method combines a novel bucket and layer spatial partitioning scheme, along with an iterative process for surface subdivision based on the optimization of quality-of-fit statistics. Our approach can efficiently decompose and approximate a dataset through the local classification and fitting of surface regions. The algorithm uses a standard least squares approach combined with Delaunay-based triangulation for developing these approximated surfaces. To demonstrate the effectiveness of our approach, we execute the algorithm on several real-world datasets scanned from complex environments. We perform an analysis of the various techniques presented and provide a comparison of our approach with the standard k-nearest neighbors method commonly used for solving this problem. Through this performance analysis we show that as the complexity of the datasets increase, the performance and accuracy of our proposed approach continues to function at an effective level.