DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913581744783360 |
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| author | Li, Hao Gao, Yuanyuan Peng, Haosong Wu, Chenming Ye, Weicai Zhan, Yufeng Zhao, Chen Zhang, Dingwen Wang, Jingdong Han, Junwei |
| author_facet | Li, Hao Gao, Yuanyuan Peng, Haosong Wu, Chenming Ye, Weicai Zhan, Yufeng Zhao, Chen Zhang, Dingwen Wang, Jingdong Han, Junwei |
| contents | Novel-view synthesis (NVS) approaches play a critical role in vast scene reconstruction. However, these methods rely heavily on dense image inputs and prolonged training times, making them unsuitable where computational resources are limited. Additionally, few-shot methods often struggle with poor reconstruction quality in vast environments. This paper presents DGTR, a novel distributed framework for efficient Gaussian reconstruction for sparse-view vast scenes. Our approach divides the scene into regions, processed independently by drones with sparse image inputs. Using a feed-forward Gaussian model, we predict high-quality Gaussian primitives, followed by a global alignment algorithm to ensure geometric consistency. Synthetic views and depth priors are incorporated to further enhance training, while a distillation-based model aggregation mechanism enables efficient reconstruction. Our method achieves high-quality large-scale scene reconstruction and novel-view synthesis in significantly reduced training times, outperforming existing approaches in both speed and scalability. We demonstrate the effectiveness of our framework on vast aerial scenes, achieving high-quality results within minutes. Code will released on our [https://3d-aigc.github.io/DGTR]. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12309 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes Li, Hao Gao, Yuanyuan Peng, Haosong Wu, Chenming Ye, Weicai Zhan, Yufeng Zhao, Chen Zhang, Dingwen Wang, Jingdong Han, Junwei Computer Vision and Pattern Recognition Novel-view synthesis (NVS) approaches play a critical role in vast scene reconstruction. However, these methods rely heavily on dense image inputs and prolonged training times, making them unsuitable where computational resources are limited. Additionally, few-shot methods often struggle with poor reconstruction quality in vast environments. This paper presents DGTR, a novel distributed framework for efficient Gaussian reconstruction for sparse-view vast scenes. Our approach divides the scene into regions, processed independently by drones with sparse image inputs. Using a feed-forward Gaussian model, we predict high-quality Gaussian primitives, followed by a global alignment algorithm to ensure geometric consistency. Synthetic views and depth priors are incorporated to further enhance training, while a distillation-based model aggregation mechanism enables efficient reconstruction. Our method achieves high-quality large-scale scene reconstruction and novel-view synthesis in significantly reduced training times, outperforming existing approaches in both speed and scalability. We demonstrate the effectiveness of our framework on vast aerial scenes, achieving high-quality results within minutes. Code will released on our [https://3d-aigc.github.io/DGTR]. |
| title | DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.12309 |