三角形泼溅SLAM
Triangle Splatting SLAM
AI总结：
提出首个使用可微三角形作为3D地图表示的密集RGB-D SLAM系统，通过在线可微渲染实现跟踪与建图，并支持实时网格转换与编辑。
摘要：
我们提出了一种密集RGB-D SLAM系统，使用可微三角形作为3D地图表示。虽然3D高斯泼溅已成为新颖视角合成的主要方法，但三角形仍然是传统渲染硬件、游戏引擎以及需要显式几何的下游任务（如模拟、碰撞和编辑）的标准图元。最近的离线方法表明，通过在一组带姿态的图像上进行Delaunay三角剖分，可以将非结构化的“三角形汤”优化为照片级逼真的网格。基于这一见解，我们提出了第一个密集SLAM系统，通过在线可微渲染三角形汤来执行跟踪和建图。地图可以通过受限Delaunay三角剖分实时转换为连通网格，从而实现网格变形和碰撞检测等新的在线功能。在Replica和TUM-RGBD数据集上，我们的系统在3D几何方面优于基线，匹配相机跟踪精度，并支持基于网格的在线场景编辑。
英文摘要：
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.
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。
