Feature-Highlighting Transparent Visualization of Laser-Scanned Point Clouds Based on Curvature-Dependent Poisson Disk Sampling

Abstract

In recent years, with the development of 3D laser-measurement technology, digital archiving is being carried out as one of the efforts to leave cultural assets to posterity around the world. The laser-scanned point clouds are large-scale and precisely record complex 3D structures of the cultural assets. Accordingly, such point clouds are used in research field of visualization to support analysis and use of the assets. As representative examples of visualization, there are feature-highlighting and transparent visualization. The quality of visualization highly depends on distributional uniformity, that is, uniformity of inter-point distances. However, laser-scanned point clouds are usually distributional bias data, which makes it impossible to visualize with high quality. In previous studies, making point distances uniform by Poisson disk sampling, the quality of transparent visualization can be improved. This study proposes curvature-dependent Poisson disk sampling. The proposed method adjusts the order and radius of the sampling disk according to the curvature calculated by principal component analysis. By applying the proposed method to laser-scanned point clouds, the edges of cultural assets can be emphasized and the visibility of shape further improves in transparent visualization. Thereby, we realize feature-highlighting transparent visualization with high visibility of three-dimensional structure and edge-shape.