Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
The upsampling of point clouds is a common task to increase the expressiveness and richness of the details. The quality of upsampled point clouds is crucial for downstream tasks, such as mesh reconstruction. With the rapid development of deep learning technology, many neural network-based methods ha...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/1/174 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549469785522176 |
|---|---|
| author | Shengwei Qin Yao Jin Hailong Hu |
| author_facet | Shengwei Qin Yao Jin Hailong Hu |
| author_sort | Shengwei Qin |
| collection | DOAJ |
| description | The upsampling of point clouds is a common task to increase the expressiveness and richness of the details. The quality of upsampled point clouds is crucial for downstream tasks, such as mesh reconstruction. With the rapid development of deep learning technology, many neural network-based methods have been proposed for point cloud upsampling. However, there are common challenges among these methods such as blurring sharper points (e.g., corner or edge points) and producing points gathered together. These problems are caused by similar feature replication or insufficient supervised information. To address these concerns, we present SSPU-FENet, a self-supervised network consisting of two modules specifically designed for geometric detail-preserved point cloud upsampling. The first module, called the feature enhancement module (FEM), aims to prevent feature blurring. This module retains important features such as edges and corners by using non-artificial encoding methods and learning mechanisms to avoid the creation of blurred points. The second module, called the 3D noise perturbation module (NPM), focuses on high-dimensional feature processing and addresses the challenges of feature similarity. This module adjusts the spacing of reconstructed points, ensuring that they are neither too close nor too far apart, thus maintaining point uniformity. In addition, SSPU-FENet proposes self-supervised loss functions that emphasize global shape consistency and local geometric structure consistency. These loss functions enable efficient network training, leading to superior upsampling results. Experimental results on various datasets show that the upsampling results of the SSPU-FENet are comparable to those of supervised learning methods and close to the ground truth (GT) point clouds. Furthermore, our evaluation metrics, such as the chamfer distance (CD, 0.0991), outperform the best methods (CD, 0.0998) in the case of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>16</mn><mo>×</mo></mrow></semantics></math></inline-formula> upsampling with 2048-point input. |
| format | Article |
| id | doaj-art-ec647b49f1bb4803bbc6cbde6eb9ed0e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ec647b49f1bb4803bbc6cbde6eb9ed0e2025-01-10T13:14:41ZengMDPI AGApplied Sciences2076-34172024-12-0115117410.3390/app15010174Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised NetworkShengwei Qin0Yao Jin1Hailong Hu2School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, ChinaZhejiang Provincial Innovation Center of Advanced Textile Technology, Shaoxing 312000, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, ChinaThe upsampling of point clouds is a common task to increase the expressiveness and richness of the details. The quality of upsampled point clouds is crucial for downstream tasks, such as mesh reconstruction. With the rapid development of deep learning technology, many neural network-based methods have been proposed for point cloud upsampling. However, there are common challenges among these methods such as blurring sharper points (e.g., corner or edge points) and producing points gathered together. These problems are caused by similar feature replication or insufficient supervised information. To address these concerns, we present SSPU-FENet, a self-supervised network consisting of two modules specifically designed for geometric detail-preserved point cloud upsampling. The first module, called the feature enhancement module (FEM), aims to prevent feature blurring. This module retains important features such as edges and corners by using non-artificial encoding methods and learning mechanisms to avoid the creation of blurred points. The second module, called the 3D noise perturbation module (NPM), focuses on high-dimensional feature processing and addresses the challenges of feature similarity. This module adjusts the spacing of reconstructed points, ensuring that they are neither too close nor too far apart, thus maintaining point uniformity. In addition, SSPU-FENet proposes self-supervised loss functions that emphasize global shape consistency and local geometric structure consistency. These loss functions enable efficient network training, leading to superior upsampling results. Experimental results on various datasets show that the upsampling results of the SSPU-FENet are comparable to those of supervised learning methods and close to the ground truth (GT) point clouds. Furthermore, our evaluation metrics, such as the chamfer distance (CD, 0.0991), outperform the best methods (CD, 0.0998) in the case of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>16</mn><mo>×</mo></mrow></semantics></math></inline-formula> upsampling with 2048-point input.https://www.mdpi.com/2076-3417/15/1/174point cloud upsamplingself-supervised learningfeature enhancementperturbation learning |
| spellingShingle | Shengwei Qin Yao Jin Hailong Hu Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network Applied Sciences point cloud upsampling self-supervised learning feature enhancement perturbation learning |
| title | Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network |
| title_full | Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network |
| title_fullStr | Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network |
| title_full_unstemmed | Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network |
| title_short | Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network |
| title_sort | geometric detail preserved point cloud upsampling via a feature enhanced self supervised network |
| topic | point cloud upsampling self-supervised learning feature enhancement perturbation learning |
| url | https://www.mdpi.com/2076-3417/15/1/174 |
| work_keys_str_mv | AT shengweiqin geometricdetailpreservedpointcloudupsamplingviaafeatureenhancedselfsupervisednetwork AT yaojin geometricdetailpreservedpointcloudupsamplingviaafeatureenhancedselfsupervisednetwork AT hailonghu geometricdetailpreservedpointcloudupsamplingviaafeatureenhancedselfsupervisednetwork |