Triangle Splatting SLAM 文章

ArXiv CS.CV2026-06-01NEWSen作者: Nicholas Fry (Software Performance Optimisation Group, Imperial College London, Department of Computing, Imperial College London), Eric Dexheimer (Department of Computing, Imperial College London), Kirill Mazur (Department of Computing, Imperial College London), Paul H. J. Kelly (Software Performance Optimisation Group, Imperial College London, Department of Computing, Imperial College London), Andrew J. Davison (Department of Computing, Imperial College London)

摘要

arXiv:2605.31419v1 Announce Type: new Abstract: 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.

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Triangle Splatting SLAM
2026-06-01PRODUCT_LAUNCH影响: MEDIUM

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