ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909659441397760 |
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| author | Li, Zhuoxiao Yao, Shanliang Wu, Taoyu Yue, Yong Zhao, Wufan Qin, Rongjun Garcia-Fernandez, Angel F. Levers, Andrew Zhu, Xiaohui |
| author_facet | Li, Zhuoxiao Yao, Shanliang Wu, Taoyu Yue, Yong Zhao, Wufan Qin, Rongjun Garcia-Fernandez, Angel F. Levers, Andrew Zhu, Xiaohui |
| contents | While Gaussian Splatting (GS) demonstrates efficient and high-quality scene rendering and small area surface extraction ability, it falls short in handling large-scale aerial image surface extraction tasks. To overcome this, we present ULSR-GS, a framework dedicated to high-fidelity surface extraction in ultra-large-scale scenes, addressing the limitations of existing GS-based mesh extraction methods. Specifically, we propose a point-to-photo partitioning approach combined with a multi-view optimal view matching principle to select the best training images for each sub-region. Additionally, during training, ULSR-GS employs a densification strategy based on multi-view geometric consistency to enhance surface extraction details. Experimental results demonstrate that ULSR-GS outperforms other state-of-the-art GS-based works on large-scale aerial photogrammetry benchmark datasets, significantly improving surface extraction accuracy in complex urban environments. Project page: https://ulsrgs.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01402 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency Li, Zhuoxiao Yao, Shanliang Wu, Taoyu Yue, Yong Zhao, Wufan Qin, Rongjun Garcia-Fernandez, Angel F. Levers, Andrew Zhu, Xiaohui Computer Vision and Pattern Recognition While Gaussian Splatting (GS) demonstrates efficient and high-quality scene rendering and small area surface extraction ability, it falls short in handling large-scale aerial image surface extraction tasks. To overcome this, we present ULSR-GS, a framework dedicated to high-fidelity surface extraction in ultra-large-scale scenes, addressing the limitations of existing GS-based mesh extraction methods. Specifically, we propose a point-to-photo partitioning approach combined with a multi-view optimal view matching principle to select the best training images for each sub-region. Additionally, during training, ULSR-GS employs a densification strategy based on multi-view geometric consistency to enhance surface extraction details. Experimental results demonstrate that ULSR-GS outperforms other state-of-the-art GS-based works on large-scale aerial photogrammetry benchmark datasets, significantly improving surface extraction accuracy in complex urban environments. Project page: https://ulsrgs.github.io. |
| title | ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.01402 |