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Article

Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization

1
Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, Shiga, Japan
2
College of Information Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, Shiga, Japan
3
Graduate School of Integrated Arts and Sciences, Tokushima University, Tokushima 770-8502, Tokushima, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Angel D. Sappa
Remote Sens. 2021, 13(13), 2526; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132526
Received: 6 May 2021 / Revised: 16 June 2021 / Accepted: 25 June 2021 / Published: 28 June 2021
Large-scale 3D-scanned point clouds enable the accurate and easy recording of complex 3D objects in the real world. The acquired point clouds often describe both the surficial and internal 3D structure of the scanned objects. The recently proposed edge-highlighted transparent visualization method is effective for recognizing the whole 3D structure of such point clouds. This visualization utilizes the degree of opacity for highlighting edges of the 3D-scanned objects, and it realizes clear transparent viewing of the entire 3D structures. However, for 3D-scanned point clouds, the quality of any edge-highlighting visualization depends on the distribution of the extracted edge points. Insufficient density, sparseness, or partial defects in the edge points can lead to unclear edge visualization. Therefore, in this paper, we propose a deep learning-based upsampling method focusing on the edge regions of 3D-scanned point clouds to generate more edge points during the 3D-edge upsampling task. The proposed upsampling network dramatically improves the point-distributional density, uniformity, and connectivity in the edge regions. The results on synthetic and scanned edge data show that our method can improve the percentage of edge points more than 15% compared to the existing point cloud upsampling network. Our upsampling network works well for both sharp and soft edges. A combined use with a noise-eliminating filter also works well. We demonstrate the effectiveness of our upsampling network by applying it to various real 3D-scanned point clouds. We also prove that the improved edge point distribution can improve the visibility of the edge-highlighted transparent visualization of complex 3D-scanned objects. View Full-Text
Keywords: transparent visualization; point upsampling; 3D-scanned point cloud; opacity-based edge highlighting; 3D edges; deep learning transparent visualization; point upsampling; 3D-scanned point cloud; opacity-based edge highlighting; 3D edges; deep learning
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MDPI and ACS Style

Li, W.; Hasegawa, K.; Li, L.; Tsukamoto, A.; Tanaka, S. Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization. Remote Sens. 2021, 13, 2526. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132526

AMA Style

Li W, Hasegawa K, Li L, Tsukamoto A, Tanaka S. Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization. Remote Sensing. 2021; 13(13):2526. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132526

Chicago/Turabian Style

Li, Weite, Kyoko Hasegawa, Liang Li, Akihiro Tsukamoto, and Satoshi Tanaka. 2021. "Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization" Remote Sensing 13, no. 13: 2526. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132526

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