RGBDS-SLAM：基于3D多级金字塔高斯溅射的RGB-D语义稠密SLAM
RGBDS-SLAM: A RGB-D Semantic Dense SLAM Based on 3D Multi Level Pyramid Gaussian Splatting
AI总结：
针对现有基于3D高斯溅射的SLAM方法忽略场景细节与一致性的问题，提出RGBDS-SLAM系统，通过3D多级金字塔高斯溅射和紧耦合多特征优化机制提升重建质量，在公开数据集上性能优于现有最先进方法。
摘要：
高质量重建对稠密SLAM至关重要。近期流行方法利用3D高斯溅射（3D GS）技术实现场景的RGB、深度和语义重建，但常忽略场景不同部分的细节与一致性问题。为此，我们提出RGBDS-SLAM——一种基于3D多级金字塔高斯溅射的RGB-D语义稠密SLAM系统，可实现场景RGB、深度和语义的高质量稠密重建。该系统引入3D多级金字塔高斯溅射方法，通过提取多级图像金字塔用于高斯溅射训练以恢复场景细节，确保RGB、深度和语义重建的一致性；还设计了紧耦合多特征重建优化机制，使RGB、深度和语义图的重建精度在渲染优化过程中相互增强。在Replica和ScanNet公开数据集上的大量定量、定性及消融实验表明，所提方法优于当前最先进方法。开源代码将发布于：https://github.com/zhenzhongcao/RGBDS-SLAM。
英文摘要：
High-quality reconstruction is crucial for dense SLAM. Recent popular approaches utilize 3D Gaussian Splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. However, these methods often overlook issues of detail and consistency in different parts of the scene. To address this, we propose RGBDS-SLAM, a RGB-D semantic dense SLAM system based on 3D multi-level pyramid gaussian splatting, which enables high-quality dense reconstruction of scene RGB, depth, and semantics.In this system, we introduce a 3D multi-level pyramid gaussian splatting method that restores scene details by extracting multi-level image pyramids for gaussian splatting training, ensuring consistency in RGB, depth, and semantic reconstructions. Additionally, we design a tightly-coupled multi-features reconstruction optimization mechanism, allowing the reconstruction accuracy of RGB, depth, and semantic maps to mutually enhance each other during the rendering optimization process. Extensive quantitative, qualitative, and ablation experiments on the Replica and ScanNet public datasets demonstrate that our proposed method outperforms current state-of-the-art methods. The open-source code will be available at: https://github.com/zhenzhongcao/RGBDS-SLAM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
