Scientific Reports· 2026Q1
An efficient large-scale scene reconstruction method based on 3D Gaussian splatting
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- Q1SCImago
- 2026year
Short summary
A new method improves 3D Gaussian Splatting for large scenes by using sparse attention and a structural dropout strategy, achieving higher memory efficiency without sacrificing reconstruction accuracy.
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Key points
- Replaces global attention with sparse attention for improved computational efficiency in 3D Gaussian Splatting.
- Introduces a structural information-guided dropout strategy to adaptively suppress redundant Gaussians.
- Achieves greater memory efficiency compared to state-of-the-art methods.
- Maintains high reconstruction accuracy for large-scale scenes.
AI-generated from the title and abstract; the full text is not read.
Abstract
Three-dimensional (3D) reconstruction based on Gaussian Splatting has achieved remarkable success due to its real-time rendering capabilities. However, for large-scale scene reconstruction, existing methods are still far from satisfactory because of the trade-off between high reconstruction quality and the heavy computational cost. In this study, we propose an efficient large-scale scene reconstruction method based on 3D Gaussian Splatting. Specifically, we replace the global attention mechanism in VGGT with a sparse attention mechanism to improve computational efficiency. Furthermore, we introduce a structural information-guided dropout strategy that adaptively suppresses redundant Gaussians in different depth regions according to their structural information. Extensive experiments on various datasets demonstrate that the proposed method is more memory-efficient than state-of-the-art methods while maintaining high reconstruction accuracy.
The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗
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Field: Computer Graphics and Computer-Aided Design
Computer Graphics and Computer-Aided DesignComputer Science