用3D高斯点云加速实时三维重建，提升稀疏视角下精度与速度。
FlashSLAM: Accelerated RGB-D SLAM for Real-Time 3D Scene Reconstruction with Gaussian Splatting
- 结合预训练特征匹配与点云配准，快速估算相机位姿
- 稀疏场景下追踪准确率提升92%，跟踪时间低于80毫秒
- 适配手机等普通设备，对深度传感器噪声有鲁棒性
我们提出FlashSLAM，一种基于3D高斯点云的新型SLAM方法，实现高效且稳健的三维场景重建。现有基于3DGS的SLAM方法在稀疏视图和大运动情况下表现不佳，因其依赖梯度下降优化，速度慢且精度低。FlashSLAM通过将3DGS与快速视觉相机跟踪技术结合，利用预训练特征匹配模型和点云配准，在不到80毫秒内完成精准位姿估计，相比SplaTAM提速90%且无需昂贵的迭代渲染。在稀疏设置下，平均追踪准确率较以往方法最高提升92%。同时，该方法考虑深度传感器噪声，增强了在智能手机等非专业设备上的鲁棒性。大量实验表明，FlashSLAM在合成与真实环境中的稀疏与密集场景下均表现稳定。基准数据集评估显示其兼具更高精度与效率，确立了其在多样化应用中作为高性能、通用化SLAM解决方案的领先地位。
原文摘要 · Abstract (English)
We present FlashSLAM, a novel SLAM approach that leverages 3D Gaussian Splatting for efficient and robust 3D scene reconstruction. Existing 3DGS-based SLAM methods often fall short in sparse view settings and during large camera movements due to their reliance on gradient descent-based optimization, which is both slow and inaccurate. FlashSLAM addresses these limitations by combining 3DGS with a fast vision-based camera tracking technique, utilizing a pretrained feature matching model and point cloud registration for precise pose estimation in under 80 ms - a 90% reduction in tracking time compared to SplaTAM - without costly iterative rendering. In sparse settings, our method achieves up to a 92% improvement in average tracking accuracy over previous methods. Additionally, it accounts for noise in depth sensors, enhancing robustness when using unspecialized devices such as smartphones. Extensive experiments show that FlashSLAM performs reliably across both sparse and dense settings, in synthetic and real-world environments. Evaluations on benchmark datasets highlight its superior accuracy and efficiency, establishing FlashSLAM as a versatile and high-performance solution for SLAM, advancing the state-of-the-art in 3D reconstruction across diverse applications.
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