Point cloud super‐resolution based on geometric constraints
Abstract Among all digital representations we have for real physical objects, three‐dimensional (3D) is arguably the most expressive encoding. But due to the limitations of 3D scanning equipment, point cloud often becomes sparse or partially missing. A point cloud super‐resolution (PCSR) method base...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-06-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cvi2.12045 |
| Summary: | Abstract Among all digital representations we have for real physical objects, three‐dimensional (3D) is arguably the most expressive encoding. But due to the limitations of 3D scanning equipment, point cloud often becomes sparse or partially missing. A point cloud super‐resolution (PCSR) method based on geometric constraints is proposed to solve the sparse problem of point clouds: it allows dense point clouds to be generated by sparse point clouds. The method is based on the conditional generative adversarial network including redesigned generator and discriminator for point cloud data specially. Moreover, the method can maintain the shape of the dense point cloud by adding geometric constraints. The contributions of our work are as follows: (1) a PCSR method based on geometric constraints is proposed; (2) add a module for obtaining point cloud neighbourhood information in the generator, called K‐nn operation module; and (3) feature aggregation is performed using the weighted pooling to process the neighbourhood information obtained by the K‐nn operation module. Extensive experimental results demonstrate the effectiveness of the proposed method. |
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| ISSN: | 1751-9632 1751-9640 |