DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View
With the widespread adoption of 3D scanning technology, depth view-driven 3D reconstruction has become crucial for applications such as SLAM, virtual reality, and autonomous vehicles. However, due to the effects of self-occlusion and environmental occlusion, obtaining complete and error-free 3D shap...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1503 |
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| author | Caixia Liu Minhong Zhu Haisheng Li Xiulan Wei Jiulin Liang Qianwen Yao |
| author_facet | Caixia Liu Minhong Zhu Haisheng Li Xiulan Wei Jiulin Liang Qianwen Yao |
| author_sort | Caixia Liu |
| collection | DOAJ |
| description | With the widespread adoption of 3D scanning technology, depth view-driven 3D reconstruction has become crucial for applications such as SLAM, virtual reality, and autonomous vehicles. However, due to the effects of self-occlusion and environmental occlusion, obtaining complete and error-free 3D shapes directly from 3D scans remains challenging, as previous reconstruction methods tend to lose details. To this end, we propose Dynamic Quality Refinement Network (DQRNet) for reconstructing complete and accurate 3D shape from a single depth view. DQRNet introduces a dynamic encoder–decoder and a detail quality refiner to generate high-resolution 3D shapes, where the former designs a dynamic latent extractor to adaptively select important parts of an object and the latter designs global and local point refiners to enhance the reconstruction quality. Experimental results show that DQRNet is able to focus on capturing the details at boundaries and key areas on ShapeNet dataset, thereby achieving better accuracy and robustness than SOTA methods. |
| format | Article |
| id | doaj-art-f2f7a8b9b8e4493dbd3cef28d9d39dc9 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-f2f7a8b9b8e4493dbd3cef28d9d39dc92025-08-20T02:52:38ZengMDPI AGSensors1424-82202025-02-01255150310.3390/s25051503DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth ViewCaixia Liu0Minhong Zhu1Haisheng Li2Xiulan Wei3Jiulin Liang4Qianwen Yao5Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, No. 33, Fucheng Road, Haidian District, Beijing 100048, ChinaBeijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, No. 33, Fucheng Road, Haidian District, Beijing 100048, ChinaBeijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, No. 33, Fucheng Road, Haidian District, Beijing 100048, ChinaSchool of Logistics, Beijing Wuzi University, No. 321, Fuhe Street, Tongzhou District, Beijing 101149, ChinaBeijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, No. 33, Fucheng Road, Haidian District, Beijing 100048, ChinaBeijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, No. 33, Fucheng Road, Haidian District, Beijing 100048, ChinaWith the widespread adoption of 3D scanning technology, depth view-driven 3D reconstruction has become crucial for applications such as SLAM, virtual reality, and autonomous vehicles. However, due to the effects of self-occlusion and environmental occlusion, obtaining complete and error-free 3D shapes directly from 3D scans remains challenging, as previous reconstruction methods tend to lose details. To this end, we propose Dynamic Quality Refinement Network (DQRNet) for reconstructing complete and accurate 3D shape from a single depth view. DQRNet introduces a dynamic encoder–decoder and a detail quality refiner to generate high-resolution 3D shapes, where the former designs a dynamic latent extractor to adaptively select important parts of an object and the latter designs global and local point refiners to enhance the reconstruction quality. Experimental results show that DQRNet is able to focus on capturing the details at boundaries and key areas on ShapeNet dataset, thereby achieving better accuracy and robustness than SOTA methods.https://www.mdpi.com/1424-8220/25/5/15033D shape completiondynamic encoder–decoderglobal and local point refinerssingle depth view |
| spellingShingle | Caixia Liu Minhong Zhu Haisheng Li Xiulan Wei Jiulin Liang Qianwen Yao DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View Sensors 3D shape completion dynamic encoder–decoder global and local point refiners single depth view |
| title | DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View |
| title_full | DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View |
| title_fullStr | DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View |
| title_full_unstemmed | DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View |
| title_short | DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View |
| title_sort | dqrnet dynamic quality refinement network for 3d reconstruction from a single depth view |
| topic | 3D shape completion dynamic encoder–decoder global and local point refiners single depth view |
| url | https://www.mdpi.com/1424-8220/25/5/1503 |
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