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- W4312693669 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." @default.
- W4312693669 created "2023-01-05" @default.
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- W4312693669 date "2022-01-01" @default.
- W4312693669 modified "2023-09-25" @default.
- W4312693669 title "Application of the Edge Upsampling Network to Soft-Edge Regions in a 3D-Scanned Point Cloud" @default.
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- W4312693669 doi "https://doi.org/10.1007/978-981-19-6857-0_2" @default.
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