用可微三角形构建实时三维地图，支持在线编辑与碰撞检测。
Triangle Splatting SLAM
- 用可微三角形作为3D地图基础，实现在线跟踪与建图。
- 在Replica和TUM-RGBD数据集上几何精度优于基线方法。
- 支持实时生成连通网格，可用于变形与碰撞检查。
我们提出一种基于可微三角形的密集RGB-D SLAM系统。尽管3D高斯点云已成为新视角合成的主流方法，三角形仍是传统渲染硬件、游戏引擎及需显式几何信息的下游任务（如仿真、碰撞检测、编辑）的标准表示。近期离线方法已证明，通过跨多张姿态图像的Delaunay三角剖分，可将无序三角形集合优化为逼真网格。在此基础上，我们首次实现基于三角形点云的在线可微渲染SLAM系统，同时完成追踪与建图。地图可通过受限Delaunay三角剖分实时转换为连通网格，支持在线网格变形与碰撞检查。在Replica和TUM-RGBD数据集上，本系统在3D几何精度上优于基线，在相机追踪精度上相当，并实现基于网格的在线场景编辑。
原文摘要 · Abstract (English)
We present a dense RGB-D SLAM system using differentiable triangles as the 3D map representation. While 3D Gaussian Splatting has emerged as the leading method for novel-view synthesis, triangles remain the standard primitive for traditional rendering hardware, game engines, and downstream tasks requiring explicit geometry such as simulation, collision, and editing. Recent offline methods have demonstrated that an unstructured 'triangle soup' can be optimised into a photorealistic mesh via Delaunay triangulation across a set of posed images. Building upon this insight, we present the first dense SLAM system to employ Triangle Splatting to perform both tracking and mapping through online differentiable rendering of a triangle soup. The map can be converted into a connected mesh on-the-fly via restricted Delaunay triangulation, enabling new online capabilities such as mesh deformation and collision checking. On Replica and TUM-RGBD, our system outperforms baselines on 3D geometry, matches the camera-tracking accuracy, and enables online mesh-based scene editing.
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