FlashSLAM：基于高斯溅射的加速RGB-D SLAM用于实时3D场景重建
FlashSLAM: Accelerated RGB-D SLAM for Real-Time 3D Scene Reconstruction with Gaussian Splatting
AI总结：
针对现有基于3D高斯溅射的SLAM方法在稀疏视角和大相机运动时的不足，FlashSLAM结合快速视觉相机跟踪技术，实现80毫秒内精确位姿估计，跟踪时间减少90%、稀疏设置精度提升92%，增强非专用设备鲁棒性。
摘要：
我们提出FlashSLAM，一种利用3D高斯溅射（3D Gaussian Splatting）实现高效且鲁棒的3D场景重建的新型SLAM方法。现有基于3DGS的SLAM方法由于依赖基于梯度下降的优化，速度慢且不准确，在稀疏视角设置和大相机运动时往往表现不佳。FlashSLAM通过将3DGS与快速视觉相机跟踪技术相结合来解决这些局限性，利用预训练特征匹配模型和点云配准实现精确位姿估计，耗时不足80毫秒——与SplaTAM相比跟踪时间减少90%，且无需代价高昂的迭代渲染。在稀疏设置中，我们的方法比现有方法平均跟踪精度提升高达92%。此外，它还能处理深度传感器噪声，增强使用智能手机等非专用设备时的鲁棒性。大量实验表明，FlashSLAM在稀疏和密集设置、合成及真实环境中均表现可靠。基准数据集上的评估凸显其卓越的精度和效率，使FlashSLAM成为SLAM的通用高性能解决方案，推动了多种应用中3D重建的技术前沿。
英文摘要：
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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