High-Visibility Edge-Highlighting Visualization of 3D Scanned Point Clouds Based on Dual 3D Edge Extraction

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 19; p. 3750
• Main Authors: Yamada, Yuri, Takatori, Satoshi, Adachi, Motoaki, Brahmantara, Hasegawa, Kyoko, Li, Liang, Pan, Jiao, Thufail, Fadjar I., Yamaguchi, Hiroshi, Tanaka, Satoshi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2024
• Subjects: 3D scanned point cloud; Cloud point curves; Clutter; Color; Color vision; Cultural heritage; Cultural resources; Depth perception; dual 3D edge extraction; edge-highlighting visualization; edge-thinning effect; Eigenvalues; Halo effect; Object recognition; Opacity; Scanning devices; Space perception; Statistical analysis; Statistical methods; Three dimensional models; Visualization; Visualization (Computers); Japan
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16193750

Recent advances in 3D scanning have enabled the digital recording of complex objects as large-scale point clouds, which require clear visualization to convey their 3D shapes effectively. Edge-highlighting visualization is used to improve the comprehensibility of complex 3D structures by enhancing the 3D edges and high-curvature regions of the scanned objects. However, traditional methods often struggle with real-world objects due to inadequate representation of soft edges (i.e., rounded edges) and excessive line clutter, impairing resolution and depth perception. To address these challenges, we propose a novel visualization method for 3D scanned point clouds based on dual 3D edge extraction and opacity–color gradation. Dual 3D edge extraction separately identifies sharp and soft edges, integrating both into the visualization. Opacity–color gradation enhances the clarity of fine structures within soft edges through variations in color and opacity, while also creating a halo effect that improves both resolution and depth perception of the visualized edges. Computation times required for dual 3D edge extraction are comparable to conventional binary statistical edge-extraction methods. Visualizations with opacity–color gradation are executable at interactive rendering speeds. The effectiveness of the proposed method is demonstrated using 3D scanned point cloud data from high-value cultural heritage objects.