{"ID":2832017,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06818","arxiv_id":"2512.06818","title":"MeshSplatting: Differentiable Rendering with Opaque Meshes","abstract":"Primitive-based splatting methods like 3D Gaussian Splatting have revolutionized novel view synthesis with real-time rendering. However, their point-based representations remain incompatible with mesh-based pipelines that power AR/VR and game engines. We present MeshSplatting, a mesh-based reconstruction approach that jointly optimizes geometry and appearance through differentiable rendering. By enforcing connectivity via restricted Delaunay triangulation and refining surface consistency, MeshSplatting creates end-to-end smooth, visually high-quality meshes that render efficiently in real-time 3D engines. On Mip-NeRF360, it boosts PSNR by +0.69 dB over the current state-of-the-art MiLo for mesh-based novel view synthesis, while training 2x faster and using 2x less memory, bridging neural rendering and interactive 3D graphics for seamless real-time scene interaction. The project page is available at https://meshsplatting.github.io/.","short_abstract":"Primitive-based splatting methods like 3D Gaussian Splatting have revolutionized novel view synthesis with real-time rendering. However, their point-based representations remain incompatible with mesh-based pipelines that power AR/VR and game engines. We present MeshSplatting, a mesh-based reconstruction approach that...","url_abs":"https://arxiv.org/abs/2512.06818","url_pdf":"https://arxiv.org/pdf/2512.06818v1","authors":"[\"Jan Held\",\"Sanghyun Son\",\"Renaud Vandeghen\",\"Daniel Rebain\",\"Matheus Gadelha\",\"Yi Zhou\",\"Anthony Cioppa\",\"Ming C. Lin\",\"Marc Van Droogenbroeck\",\"Andrea Tagliasacchi\"]","published":"2025-12-07T12:31:04Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}