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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2512.06818 |
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| _version_ | 1866909947843837952 |
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| author | Held, Jan Son, Sanghyun Vandeghen, Renaud Rebain, Daniel Gadelha, Matheus Zhou, Yi Cioppa, Anthony Lin, Ming C. Van Droogenbroeck, Marc Tagliasacchi, Andrea |
| author_facet | Held, Jan Son, Sanghyun Vandeghen, Renaud Rebain, Daniel Gadelha, Matheus Zhou, Yi Cioppa, Anthony Lin, Ming C. Van Droogenbroeck, Marc Tagliasacchi, Andrea |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06818 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MeshSplatting: Differentiable Rendering with Opaque Meshes Held, Jan Son, Sanghyun Vandeghen, Renaud Rebain, Daniel Gadelha, Matheus Zhou, Yi Cioppa, Anthony Lin, Ming C. Van Droogenbroeck, Marc Tagliasacchi, Andrea Computer Vision and Pattern Recognition 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/. |
| title | MeshSplatting: Differentiable Rendering with Opaque Meshes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.06818 |