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MeshSplatBench: A Unified Benchmark for Triangle- and Mesh-Based Neural Rendering — arXiv:2609.01306

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01306
License (as archived): CC0-1.0
Archived: 2026-10-17T07:14:05.965Z
SHA-256 of the archived text: fd6b411f686d07c8634de4ec2dce55831d1fc321714db8dd2312b9ff5c000720
Stable URL of this page: https://metraipolis.com/documento/dc_3213ad577c8a

How to cite

MeshSplatBench: A Unified Benchmark for Triangle- and Mesh-Based Neural Rendering — arXiv:2609.01306 — arXiv (en), https://arxiv.org/abs/2609.01306 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3213ad577c8a (Metraipolis archive dc_3213ad577c8a, sha256 fd6b411f686d07c8634de4ec2dce55831d1fc321714db8dd2312b9ff5c000720)

BibTeX

@misc{dc3213ad577c8a,
  title = {MeshSplatBench: A Unified Benchmark for Triangle- and Mesh-Based Neural Rendering — arXiv:2609.01306},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01306},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3213ad577c8a; sha256 fd6b411f686d07c8634de4ec2dce55831d1fc321714db8dd2312b9ff5c000720},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01306. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Triangle- and mesh-based neural rendering seeks to bridge neural representations and graphics engines through triangle primitives compatible with standard rasterization. Yet existing methods were developed and evaluated under inconsistent settings, with few direct comparisons and little evaluation in graphics engines. We introduce MeshSuite, the first benchmark for systematic evaluation of engine deployment and Non-Engine Render in triangle- and mesh-based neural rendering. MeshSuite evaluates two deployment settings: Standard Deployment, using an opaque mesh pipeline with vertex colors and hardware Z-buffering; and Dedicated Deployment, using method-specific engine implementations to preserve appearance and compositing features. For mesh splatting, we also audit exported surfaces for topological and geometric integrity as downstream graphics assets. Our results show that engine deployment reduces image quality in both settings; among evaluated methods, MeshSplatting degrades least under Standard Deployment. Dedicated Deployment preserves most fidelity at roughly 5-33x the runtime of Standard Deployment across two deployment datasets. For mesh splatting, explicit connectivity and shared indexing remain limited: shared vertex indexing alone guarantees neither manifoldness nor global connectivity. These findings show that rasterizability is only part of graphics readiness and underscore the need to assess engine deployment. Project page: this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-02 in cs.CV).
Categorie arXiv: Graphics (cs.GR) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.01306 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:00:59.023Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).