Loading...
Please wait, while we are loading the content...
Similar Documents
Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization
| Content Provider | MDPI |
|---|---|
| Author | Li, Weite Hasegawa, Kyoko Li, Liang Tsukamoto, Akihiro Tanaka, Satoshi |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 2526 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13132526 |
| Journal | Remote Sensing |
| Issue Number | 13 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-06-28 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Transparent Visualization Point Upsampling 3d-scanned Point Cloud Opacity-based Edge Highlighting 3d Edges Deep Learning |
| Content Type | Text |
| Resource Type | Article |
