Abstract
Large-scale 3D scanning data based on point clouds enable accurate and fast recording of complex objects in the real world. The edges in a scanned point cloud usually describe the complex 3D structure of the target object and the surrounding scene. The recently proposed deep learning-based edge upsampling network can generate new points in the edge regions. When combined with the edge-highlighted transparent visualization method, this network can effectively improve the visibility of the edge regions in 3D-scanned point clouds. However, most previous upsampling experiments were performed on the sharp-edge regions despite that 3D-scanned objects usually contain both sharp and soft edge regions. In this paper, to demonstrate the performance of the upsampling network on soft-edge regions, we add more polygon models that contain soft edges by adjusting the models in the training set so that the network can learn more features of soft-edge regions. Additionally, we apply the upsampling network to real 3D-scanned point cloud data that contain numerous soft edges to verify that the edge upsampling network is equally effective at the upsampling task on soft-edge regions. The experimental results show that the visibility of the complex 3D-scanned objects can be effectively improved by increasing the point density in the soft-edge regions.
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Acknowledgments
The authors would like to thank the Tokushima Castle Museum for its cooperation in executing the 3D scanning. The images of the Borobudur temple are presented with the permission of the Borobudur Conservation Office and Research Center for Area Studies (P2W) of the Indonesian Institute of Sciences (LIPI). This work is partially supported by JSPS KAKENHI Grant Numbers 19KK0256 and 21H04903, and the Program for Asia-Japan Research Development (Ritsumeikan University, Japan).
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Li, W. et al. (2022). Application of the Edge Upsampling Network to Soft-Edge Regions in a 3D-Scanned Point Cloud. In: Chang, BY., Choi, C. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2021. Communications in Computer and Information Science, vol 1636. Springer, Singapore. https://doi.org/10.1007/978-981-19-6857-0_2
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DOI: https://doi.org/10.1007/978-981-19-6857-0_2
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