实时高保真场景重建新方法，用自适应优化提升3D高斯点渲染效率。
CaRtGS: Computational Alignment for Real-Time Gaussian Splatting SLAM
- 通过自适应策略优化迭代次数与点云稀疏化过程
- 在Replica等数据集上用更少的高斯点实现更高保真度渲染
- 适合需要实时、高质量三维重建的研究与应用
同时定位与建图（SLAM）在机器人领域至关重要，而真实感场景重建成为关键挑战。为此，我们提出计算对齐的实时高斯点溅射SLAM（CaRtGS），一种提升实时环境下真实感场景重建效率与质量的新方法。基于3D高斯溅射（3DGS），CaRtGS实现了优异的渲染质量和处理速度，对真实感重建尤为关键。该方法通过自适应策略解决高斯溅射SLAM（GS-SLAM）中的计算错位问题，增强优化迭代次数，缓解长尾优化难题，并改进点云稠密化过程。在Replica、TUM-RGBD和VEVector数据集上的实验表明，CaRtGS能以更少的高斯原语实现高保真渲染。本工作推动了SLAM向实时、真实感稠密渲染发展，显著提升了真实感场景表示能力。为促进研究，代码与配套视频已在项目网站公开：https://dapengfeng.github.io/cartgs。
原文摘要 · Abstract (English)
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 enhances optimization iterations, addresses long-tail optimization, and refines densification. Experiments on Replica, TUM-RGBD, and VECtor 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 and accompanying videos on our project website: https://dapengfeng.github.io/cartgs.
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