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IJAT Vol.12 No.3 pp. 348-355
doi: 10.20965/ijat.2018.p0348
(2018)

Paper:

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**, Atsushi Okamoto***, and Satoshi Tanaka*,†

*College of Information Science and Engineering, Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu-shi, Shiga 525-8577, Japan

Corresponding author

**Nara National Research Institute for Cultural Properties, Nara, Japan

***History Research Institute, Otemae University, Hyogo, Japan

Received:
August 23, 2017
Accepted:
March 16, 2018
Online released:
May 1, 2018
Published:
May 5, 2018
Keywords:
laser-scanned point cloud, transparent rendering, cultural heritage objects
Abstract

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.

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Cite this article as:
Kyoko Hasegawa, Liang Li, Naoya Okamoto, Shu Yanai, Hiroshi Yamaguchi, Atsushi Okamoto, and Satoshi Tanaka, “Application of Stochastic Point-Based Rendering to Laser-Scanned Point Clouds of Various Cultural Heritage Objects,” Int. J. Automation Technol., Vol.12, No.3, pp. 348-355, 2018
Kyoko Hasegawa, Liang Li, Naoya Okamoto, Shu Yanai, Hiroshi Yamaguchi, Atsushi Okamoto, and Satoshi Tanaka, Int. J. Automation Technol., Vol.12, No.3, pp. 348-355, 2018

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Last updated on May. 19, 2018