SEGS-SLAM

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 input image stream is processed by the localization and geometric mapping modules, generating point cloud and accurate poses. Structure-Enhanced Photorealistic Mapping (SEPM) incrementally initializes anchor points (middle) based on the point cloud (top left), which preserves the underlying structure. The poses are then fed into the Appearance-from-Motion embedding (AfME) to model appearance variations in the scene. Additionally, we introduce frequency pyramid regularization (FPR) to improve the reconstruction of high-frequency details in the scene.