FFMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
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| Publicado en: | Applied Sciences vol. 15, no. 5 (2025), p. 2472 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2076-3417 | ||
| 024 | 7 | |a 10.3390/app15052472 |2 doi | |
| 035 | |a 3176313458 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Qiu, Jiacun |u Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China; <email>qiujc@sari.ac.cn</email> (J.Q.); <email>hanzq@sari.ac.cn</email> (Z.H.); <email>zhangjl@sari.ac.cn</email> (J.Z.); University of Chinese Academy of Sciences, Beijing 100049, China | |
| 245 | 1 | |a FFMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Point cloud registration is a fundamental problem in computer vision and 3D computing, aiming to align point cloud data from different sensors or viewpoints into a unified coordinate system. In recent years, the rapid development of RGB-D sensor technology has greatly facilitated the acquisition of RGB-D data. In previous unsupervised point cloud registration methods based on RGB-D data, there has often been an overemphasis on matching local features, while the potential value of global information has been overlooked, thus limiting the improvement in registration performance. To address this issue, this paper proposes a self-attention-based global information attention module, which learns the global context of fused RGB-D features and effectively integrates global information into each individual feature. Furthermore, this paper introduces alternating self-attention and cross-attention layers, enabling the final feature fusion to achieve a broader global receptive field, thereby facilitating more precise matching relationships. We conduct extensive experiments on the ScanNet and 3DMatch datasets, and the results show that, compared to the previous state-of-the-art methods, our approach reduces the average rotation error by 26.9% and 32% on the ScanNet and 3DMatch datasets, respectively. Our method also achieves state-of-the-art performance on other key metrics. | |
| 653 | |a Accuracy | ||
| 653 | |a 3-D graphics | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Computer graphics | ||
| 653 | |a Algorithms | ||
| 653 | |a Registration | ||
| 653 | |a Computer vision | ||
| 653 | |a Neural networks | ||
| 653 | |a Sensors | ||
| 653 | |a Optimization | ||
| 653 | |a Semantics | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Han, Zhenqi |u Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China; <email>qiujc@sari.ac.cn</email> (J.Q.); <email>hanzq@sari.ac.cn</email> (Z.H.); <email>zhangjl@sari.ac.cn</email> (J.Z.) | |
| 700 | 1 | |a Liu, Lizhaung |u Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China; <email>qiujc@sari.ac.cn</email> (J.Q.); <email>hanzq@sari.ac.cn</email> (Z.H.); <email>zhangjl@sari.ac.cn</email> (J.Z.) | |
| 700 | 1 | |a Zhang, Jialu |u Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China; <email>qiujc@sari.ac.cn</email> (J.Q.); <email>hanzq@sari.ac.cn</email> (Z.H.); <email>zhangjl@sari.ac.cn</email> (J.Z.) | |
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 5 (2025), p. 2472 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3176313458/abstract/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3176313458/fulltextwithgraphics/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3176313458/fulltextPDF/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |