ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency

• URL: http://arxiv.org/abs/2412.01402v1
• Date: Mon, 02 Dec 2024 11:42:35 GMT
• Title: ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency
• Authors: Zhuoxiao Li, Shanliang Yao, Qizhong Gao, Angel F. Garcia-Fernandez, Yong Yue, Xiaohui Zhu,
• Abstract summary: We present ULSR-GS, a framework dedicated to high-fidelity surface extraction in ultra-large-scale scenes.<n>Specifically, we propose a point-to-photo partitioning approach combined with a multi-view optimal view matching principle.<n>During training, ULSR-GS employs a densification strategy based on multi-view geometric consistency to enhance surface extraction details.
• Score: 2.183054716058417
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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