# MeshSplatting: Differentiable Rendering with Opaque Meshes

arXiv: https://arxiv.org/abs/2512.06818 · HTML: https://mlsanity.com/paper/2512.06818/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2025.

## Built on by (most cited first, 20 of 20)

- [From Blobs to Spokes: High-Fidelity Surface Reconstruction via Oriented Gaussians](https://mlsanity.com/paper/2604.07337/) (2026)
- [PLANING: A Loosely Coupled Triangle-Gaussian Framework for Streaming 3D Reconstruction](https://mlsanity.com/paper/2601.22046/) (2026)
- [ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstruction](https://mlsanity.com/paper/2601.06997/) (2026)
- [ExMesh: Explicit Mesh Reconstruction with Topology Adaptation](https://mlsanity.com/paper/2606.07288/) (2026)
- [Fourier Splatting: Generalized Fourier encoded primitives for scalable radiance fields](https://mlsanity.com/paper/2603.19834/) (2026)
- [ArtMesh: Part-Aware Articulated Mesh Fields with Motion-Consistent Dynamics](https://mlsanity.com/paper/2605.16582/) (2026)
- [SoccerNet 2026 Challenges Results](https://mlsanity.com/paper/2607.07320/) (2026)
- [FTSplat: Feed-forward Triangle Splatting Network](https://mlsanity.com/paper/2603.05932/) (2026)
- [Geo-ID: Test-Time Geometric Consensus for Cross-View Consistent Intrinsics](https://mlsanity.com/paper/2603.13859/) (2026)
- [Learning View-Dependent Splatting Kernels](https://mlsanity.com/paper/2605.25426/) (2026)
- [City-Mesh3R: Simulation-Ready City-Scale 3D Mesh Reconstruction from Multi-View Images](https://mlsanity.com/paper/2605.30310/) (2026)
- [Triangle Splatting SLAM](https://mlsanity.com/paper/2605.31419/) (2026)
- [FLAT: Feedforward Latent Triangle Splatting for Geometrically Accurate Scene Generation](https://mlsanity.com/paper/2606.24876/) (2026)
- [Elastic Triangle Splatting](https://mlsanity.com/paper/2608.29106/) (2026)
- [ObjectSplat: Improving Mesh Fidelity and Interactivity for 3D Scenes via Object-Level Mesh Splatting](https://mlsanity.com/paper/2608.30423/) (2026)
- [MeshSplatBench: A Unified Benchmark for Triangle- and Mesh-Based Neural Rendering](https://mlsanity.com/paper/2609.01306/) (2026)
- [Compact Neural Appearance Models for Efficient Gaussian Splatting](https://mlsanity.com/paper/2609.05255/) (2026)
- [ReVeal: A Reconstruction-Aware Real-to-Sim Framework for VLA Policy Evaluation](https://mlsanity.com/paper/2609.23910/) (2026)
- [PowerSim: Differentiable Physics Simulation and Rendering with Power Diagrams](https://mlsanity.com/paper/2609.38153/) (2026)
- [NovaSplat: Model-Free 6D Object Pose Estimation via 3D Gaussian Splatting Onboarding and Refinement](https://mlsanity.com/paper/2610.13309/) (2026)

## Abstract (arXiv)

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/.