mlsanityData to 7 Oct 2026

Meshtryoshka: Differentiable Rendering of Real-World Scenes via Mesh Rasterization

arXiv 2606.28622 ↗First posted 26 Jun 2026cs.CV cs.GRcited by 0

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Abstract (arXiv)

Differentiable rendering has emerged as a powerful approach for 3D reconstruction and novel view synthesis. State-of-the-art differentiable rendering methods combine a variety of custom representations of 3D geometry and appearance with specialized renderers. However, most downstream tasks in computer graphics rely on 3D meshes. While prior work has attempted differentiable rendering with mesh representations, these approaches are limited to object-centric scenes and fail to reconstruct large-scale, unbounded scenes. In this work, we introduce Meshtryoshka, a novel mesh differentiable rendering framework that combines an off-the-shelf triangle rasterizer with a 3D representation that consists of nested mesh shells which resemble a matryoshka doll. In every forward pass, the mesh shells are extracted anew from a 3D signed distance function via iso-surface extraction, and the opacities for each vertex are computed as a function of signed distance. Each mesh shell is then rasterized independently, and the final image is created via alpha compositing. Crucially, mesh vertex positions are updated only indirectly via gradients that flow through the opacity values into the signed distance function, and hence, our method is compatible with off-the-shelf mesh renderers that need not be differentiable with respect to vertex positions. On object-centric scenes, our method performs competitively with surface-based differentiable rendering techniques. Our differentiable mesh rendering method scales to unbounded, real-world 3D scenes, where it yields high-quality novel view synthesis results approaching those of state-of-the-art, non-mesh methods. Our method suggests that it may be possible to solve the differentiable rendering problem without relying on specialized renderers, only using conventional tools from the computer graphics toolbox.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 50 references that are in the map. First posted on arXiv 26 Jun 2026.

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30 of 50 references.

References · 30

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
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  8. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
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  11. 2021Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesiscited by 459
  12. 2020Modular Primitives for High-Performance Differentiable Renderingcited by 439
18 more
  1. 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
  2. 2021Extracting Triangular 3D Models, Materials, and Lighting From Imagescited by 363
  3. 2022NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstructioncited by 311
  4. 2022MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architecturescited by 308
  5. 2021DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networkscited by 232
  6. 2023BakedSDF: Meshing Neural SDFs for Real-Time View Synthesiscited by 220
  7. 2023MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenescited by 215
  8. 2020MeshSDF: Differentiable Iso-Surface Extractioncited by 112
  9. 2020Learning Deformable Tetrahedral Meshes for 3D Reconstructioncited by 84
  10. 2023Adaptive Shells for Efficient Neural Radiance Field Renderingcited by 69
  11. 2025Triangle Splatting for Real-Time Radiance Field Renderingcited by 50
  12. 2024Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesiscited by 41
  13. 2023NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Supportcited by 25
  14. 2020Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracingcited by 10
  15. 2024Transforming a Non-Differentiable Rasterizer into a Differentiable One with Stochastic Gradient Estimationcited by 5
  16. 2024Volumetric Surfaces: Representing Fuzzy Geometries with Layered Meshescited by 5
  17. 20252D Triangle Splatting for Direct Differentiable Mesh Trainingcited by 5
  18. 2025Radiant Triangle Soup with Soft Connectivity Forces for 3D Reconstruction and Novel View Synthesiscited by 2

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