SEGS-SLAM: Structure-enhanced 3D Gaussian Splatting SLAM with Appearance Embedding
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915384749195264 |
|---|---|
| author | Wen, Tianci Liu, Zhiang Fang, Yongchun |
| author_facet | Wen, Tianci Liu, Zhiang Fang, Yongchun |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05242 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SEGS-SLAM: Structure-enhanced 3D Gaussian Splatting SLAM with Appearance Embedding Wen, Tianci Liu, Zhiang Fang, Yongchun Computer Vision and Pattern Recognition 68T40(Primary)68T45, 68U99 (Secondary) I.4.8; I.3.7 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/. |
| title | SEGS-SLAM: Structure-enhanced 3D Gaussian Splatting SLAM with Appearance Embedding |
| topic | Computer Vision and Pattern Recognition 68T40(Primary)68T45, 68U99 (Secondary) I.4.8; I.3.7 |
| url | https://arxiv.org/abs/2501.05242 |