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MeshSplatting: Differentiable Rendering with Opaque Meshes

About

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/.

Jan Held, Sanghyun Son, Renaud Vandeghen, Daniel Rebain, Matheus Gadelha, Yi Zhou, Anthony Cioppa, Ming C. Lin, Marc Van Droogenbroeck, Andrea Tagliasacchi• 2025

Related benchmarks

TaskDatasetResultRank
Novel View SynthesisMip-NeRF360
PSNR24.78
138
Novel View SynthesisMip-NeRF360 (test)--
62
3D ReconstructionDTU
Average Error0.79
47
Appearance RenderingScanNet V2
PSNR25.64
19
Appearance RenderingFAST-LIVO2
PSNR25.78
17
Appearance RenderingScanNet++
PSNR27.71
14
Appearance RenderingVR-NeRF
PSNR25.23
14
Appearance RenderingKITTI
PSNR17.35
14
Appearance RenderingWaymo
PSNR23.1
14
Novel View SynthesisTanks&Temples
PSNR20.52
8
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