仅RGB的高斯点溅SLAM用于无界户外场景
RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes
AI总结：
本研究提出了一种仅使用RGB的高斯点溅SLAM方法，用于无界户外场景，通过点图回归网络和端到端可微流程提升跟踪精度和新型视图合成效果。
摘要：
3D高斯点溅（3DGS）已成为SLAM中的流行解决方案，因为它能够生成高质量的新型视图。然而，先前基于高斯点溅的方法主要针对室内场景，并依赖于RGB-D传感器或预训练的深度估计模型，因此在户外场景中表现不佳。为了解决这个问题，我们提出了一种仅使用RGB的高斯点溅SLAM方法，用于无界户外场景——OpenGS-SLAM。技术上，我们首先使用点图回归网络生成帧之间的点图，以进行姿态估计。与常用的深度图相比，点图在多个视图中包含空间关系和场景几何结构，能够实现稳健的相机姿态估计。然后，我们提出将估计的相机姿态与3DGS渲染整合为一个端到端可微的流程。我们的方法实现了相机姿态和3DGS场景参数的同时优化，显著提高了系统跟踪精度。具体而言，我们还设计了一个自适应的缩放映射器，用于点图回归网络，这为点图映射到3DGS地图表示提供了更准确的映射。我们的实验在Waymo数据集上表明，OpenGS-SLAM将跟踪误差减少到先前3DGS方法的9.8%，并在新型视图合成中实现了最先进的结果。项目页面：https://3dagentworld.github.io/opengs-slam/
英文摘要：
3D Gaussian Splatting (3DGS) has become a popular solution in SLAM, as it can produce high-fidelity novel views. However, previous GS-based methods primarily target indoor scenes and rely on RGB-D sensors or pre-trained depth estimation models, hence underperforming in outdoor scenarios. To address this issue, we propose a RGB-only gaussian splatting SLAM method for unbounded outdoor scenes--OpenGS-SLAM. Technically, we first employ a pointmap regression network to generate consistent pointmaps between frames for pose estimation. Compared to commonly used depth maps, pointmaps include spatial relationships and scene geometry across multiple views, enabling robust camera pose estimation. Then, we propose integrating the estimated camera poses with 3DGS rendering as an end-to-end differentiable pipeline. Our method achieves simultaneous optimization of camera poses and 3DGS scene parameters, significantly enhancing system tracking accuracy. Specifically, we also design an adaptive scale mapper for the pointmap regression network, which provides more accurate pointmap mapping to the 3DGS map representation. Our experiments on the Waymo dataset demonstrate that OpenGS-SLAM reduces tracking error to 9.8\% of previous 3DGS methods, and achieves state-of-the-art results in novel view synthesis. Project Page: https://3dagentworld.github.io/opengs-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。
