Application of Stochastic Point-Based Rendering to Laser-Scanned Point Clouds of Various Cultural Heritage Objects
Kyoko Hasegawa, Liang Li, Naoya Okamoto, Shu Yanai, Hiroshi Yamaguchi, A. Okamoto, Satoshi Tanaka · International Journal of Automation Technology · 2018
Recently, we proposed stochastic point-based rendering, which enables precise and interactive-speed transparent rendering of large-scale laser-scanned point clouds. This transparent visualization method does not suffer from rendering artifact and realizes correct depth feel in the created 3D image. In this paper, we apply the method to several kinds of large-scale laser-scanned point clouds of cultural heritage objects and prove its wide applicability. In addition, we prove better image quality is realized by properly eliminating points to realize better distributional uniformity of points. Here, the distributional uniformity means uniformity of inter-point distances between nearest-neighbor points. We also demonstrate that highlighting feature regions, especially edges, in the transparent visualization helps us understand 3D internal structures of complex laser-scanned objects. The feature regions are highlighted by properly increasing local opacity of the regions.