仅用摄像头实现快速精准3D建图，比前代快5.57倍。
MonoGS++: Fast and Accurate Monocular RGB Gaussian SLAM
- 用动态插入和清晰度增强提升3D高斯点云质量
- 在真实与合成数据上达到顶尖定位精度
- 适合移动端或低硬件要求的实时建图场景
我们提出MonoGS++，一种基于RGB输入的快速高精度单目SLAM方法，采用3D高斯表示。相比依赖深度传感器的先前高斯溅射方法，本方法仅需摄像头，通过在线视觉里程计实时生成稀疏点云。为减少冗余并提升重建质量，引入动态高斯点插入、清晰度增强的稠密化模块及平面正则化，有效处理无纹理区域和平面表面。在合成数据集Replica和真实数据集TUM-RGBD上，相机跟踪精度媲美当前最优水平。此外，在帧率上相较此前最优方法MonoGS提升5.57倍，实现高效实时性能。
原文摘要 · Abstract (English)
We present MonoGS++, a novel fast and accurate Simultaneous Localization and Mapping (SLAM) method that leverages 3D Gaussian representations and operates solely on RGB inputs. While previous 3D Gaussian Splatting (GS)-based methods largely depended on depth sensors, our approach reduces the hardware dependency and only requires RGB input, leveraging online visual odometry (VO) to generate sparse point clouds in real-time. To reduce redundancy and enhance the quality of 3D scene reconstruction, we implemented a series of methodological enhancements in 3D Gaussian mapping. Firstly, we introduced dynamic 3D Gaussian insertion to avoid adding redundant Gaussians in previously well-reconstructed areas. Secondly, we introduced clarity-enhancing Gaussian densification module and planar regularization to handle texture-less areas and flat surfaces better. We achieved precise camera tracking results both on the synthetic Replica and real-world TUM-RGBD datasets, comparable to those of the state-of-the-art. Additionally, our method realized a significant 5.57x improvement in frames per second (fps) over the previous state-of-the-art, MonoGS.
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