SGAD-SLAM：在调整深度处泼溅高斯以提升 RGBD SLAM 中的辐射场
SGAD-SLAM: Splatting Gaussians at Adjusted Depth for Better Radiance Fields in RGBD SLAM
把像素对齐的高斯沿射线方向做深度调整，并用逐像素深度高斯分布做帧到场景的快速对齐，这套「射线可调高斯 + 深度分布配准」可直接迁移到其他 RGBD 建图/跟踪与实时新视角合成系统。
原文依据 we adopt pixel-aligned Gaussians but allow each Gaussian to adjust its position along its ray
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论3D 高斯泼溅（3DGS）在 RGBD SLAM 中已取得显著进展。现有方法通常使用 3D 高斯或与视角绑定的 3D 高斯来表示跟踪与建图中的辐射场。然而，这些高斯要么过于灵活，要么运动受限，导致收敛缓慢或渲染质量受限。为解决该问题，我们采用像素对齐的高斯，但允许每个高斯沿其射线调整位置以最大化渲染质量，即使在高斯被简化以提升系统可扩展性的情况下也是如此。为加速跟踪，我们将每个像素周围的深度分布建模为高斯分布，然后利用这些分布快速地将每一帧对齐到三维场景。我们在广泛使用的基准上报告了评测结果，验证了设计的合理性，并展示了在视角渲染、相机跟踪、运行时间与存储复杂度方面相对于最新方法的优势。代码与视频见项目主页 https://machineperceptionlab.github.io/SGAD-SLAM-Project。
英文原文摘要 1024 字符
3D Gaussian Splatting (3DGS) has made remarkable progress in RGBD SLAM. Current methods usually use 3D Gaussians or view-tied 3D Gaussians to represent radiance fields in tracking and mapping. However, these Gaussians are either too flexible or too limited in movements, resulting in slow convergence or limited rendering quality. To resolve this issue, we adopt pixel-aligned Gaussians but allow each Gaussian to adjust its position along its ray to maximize the rendering quality, even if Gaussians are simplified to improve system scalability. To speed up the tracking, we model the depth distribution around each pixel as a Gaussian distribution, and then use these distributions to align each frame to the 3D scene quickly. We report our evaluations on widely used benchmarks, justify our designs, and show advantages over the latest methods in view rendering, camera tracking, runtime, and storage complexity. Please see our project page for code and videos at https://machineperceptionlab.github.io/SGAD-SLAM-Project.