Simultaneous Localization and Mapping (SLAM) is pivotal in robotics, with photorealistic scene reconstruction emerging as a key challenge. To address this, we introduce Computational Alignment for Real-Time Gaussian Splatting SLAM (CaRtGS), a novel method enhancing the efficiency and quality of photorealistic scene reconstruction in real-time environments. Leveraging 3D Gaussian Splatting (3DGS), CaRtGS achieves superior rendering quality and processing speed, which is crucial for scene photorealistic reconstruction. Our approach tackles computational misalignment in Gaussian Splatting SLAM (GS-SLAM) through an adaptive strategy that optimizes training, addresses long-tail optimization, and refines densification. Experiments on Replica and TUM-RGBD datasets demonstrate CaRtGS's effectiveness in achieving high-fidelity rendering with fewer Gaussian primitives. This work propels SLAM towards real-time, photorealistic dense rendering, significantly advancing photorealistic scene representation. For the benefit of the research community, we release the code on our project website: this https URL.
同时定位与建图（SLAM）是机器人技术中的关键环节，而逼真的场景重建则是面临的主要挑战之一。为了解决这一问题，我们提出了用于实时高斯分布SLAM的计算对齐方法（Computational Alignment for Real-Time Gaussian Splatting SLAM，CaRtGS），这是一种新颖的方法，旨在提高实时环境中逼真场景重建的效率和质量。通过利用三维高斯分布（3D Gaussian Splatting, 3DGS），CaRtGS 实现了卓越的渲染质量和处理速度，对于逼真的场景重建至关重要。我们的方法通过自适应策略解决了高斯分布 SLAM（GS-SLAM）中的计算错位问题，优化了训练过程，处理了长尾优化问题，并改进了稠密化。我们在 Replica 和 TUM-RGBD 数据集上的实验表明，CaRtGS 在使用更少的高斯基元的情况下实现了高保真的渲染效果。本研究显著推动了 SLAM 向实时、逼真密集渲染的发展，极大地提升了逼真场景表示的水平。