提升3D高斯点云的结构一致性与视觉质量，实现更逼真的实时建图。
SEGS-SLAM: Structure-enhanced 3D Gaussian Splatting SLAM with Appearance Embedding
- 用结构化点云初始化3D高斯，增强几何一致性。
- 在TUM RGB-D数据集上比MonoGS提升19.86% PSNR。
- 适合追求高质量视觉重建的机器人或AR应用开发者。
3D高斯点云（3D-GS）近期革新了同时定位与地图构建（SLAM）中的新视角合成。然而，现有方法未能充分捕捉底层结构，导致结构不一致；同时难以应对突变的外观变化，影响视觉质量。为此，我们提出SEGS-SLAM，一种结构增强型3D高斯点云SLAM方法，可实现高质量的逼真建图。主要贡献有二：首先，提出结构增强的逼真建图（SEPM）框架，首次利用高度结构化的点云初始化结构化3D高斯，显著提升渲染质量；其次，提出基于运动的外观嵌入（AfME），使3D高斯能更好建模不同相机位姿下的图像外观变化。在单目、双目和RGB-D数据集上的大量实验表明，SEGS-SLAM在逼真建图质量上显著优于当前最优方法，例如在单目相机的TUM RGB-D数据集上，PSNR相比MonoGS提升19.86%。项目主页见https://segs-slam.github.io/。
原文摘要 · Abstract (English)
3D Gaussian splatting (3D-GS) has recently revolutionized novel view synthesis in the simultaneous localization and mapping (SLAM) problem. However, most existing algorithms fail to fully capture the underlying structure, resulting in structural inconsistency. Additionally, they struggle with abrupt appearance variations, leading to inconsistent visual quality. To address these problems, we propose SEGS-SLAM, a structure-enhanced 3D Gaussian Splatting SLAM, which achieves high-quality photorealistic mapping. Our main contributions are two-fold. First, we propose a structure-enhanced photorealistic mapping (SEPM) framework that, for the first time, leverages highly structured point cloud to initialize structured 3D Gaussians, leading to significant improvements in rendering quality. Second, we propose Appearance-from-Motion embedding (AfME), enabling 3D Gaussians to better model image appearance variations across different camera poses. Extensive experiments on monocular, stereo, and RGB-D datasets demonstrate that SEGS-SLAM significantly outperforms state-of-the-art (SOTA) methods in photorealistic mapping quality, e.g., an improvement of $19.86\%$ in PSNR over MonoGS on the TUM RGB-D dataset for monocular cameras. The project page is available at https://segs-slam.github.io/.
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