Content-Aware Point Cloud Simplification of Natural Scenes

Laser scanning technology is becoming ubiquitous in studies involving 3-D characterizations of natural scenes, e.g., for geomorphological or archeological interpretations. Setting the point density in such scanning campaigns is usually dictated by the objects of interest within the site yet is applied to the entire scene. Such campaigns result in large data volumes, which are difficult to analyze and where the objects of interest may be hidden in the redundant data. To reduce these excessive volumes, existing simplification strategies maintain smoothness and preserve discontinuities in the point cloud but disregard the need to preserve detail at the regions of interest (ROIs). To address that, this article proposes a new, context-aware, subsampling approach that retains the high resolution of objects of interest while reducing the data load of less important regions. To do so, we identify the ROI by means of visual saliency measures and reduce the data volume only at the nonsalient regions. To facilitate progressive subsampling, the reduction is based on a hierarchical data structure that is surficial in nature. In this way, the retained representative points describe the underlying surface rather than an interpolation of it. We demonstrate our proposed model on datasets originating from different scanners that feature a variety of scenes. We compare our results to three common simplification approaches. Our results show a reduced point cloud that is similar to the original and allows analysis of ROI at the required point resolution, regardless of the level of simplification.