MeshSplatBench：基于三角形的神经渲染统一基准

中文摘要 AI 辅助

基于三角形的神经渲染通过优化与标准光栅化硬件兼容的显式几何基元，连接了神经场景表示与传统图形管线。然而，现有方法几乎仅在自定义研究渲染器中评估，模糊了其在生产引擎中的实际部署可行性。为弥合这一差距，我们推出MeshSplatBench，这是一个统一基准，系统研究从原生优化到游戏引擎部署全流程的基于三角形的神经渲染。MeshSplatBench建立了标准化评估协议，同时保留各方法的原生优化语义，在0.8%的PSNR偏差内复现已发表结果。此外，我们推出分层Unity部署协议，涵盖三个渲染层级：原生CUDA渲染器、方法专用引擎着色器、标准不透明网格管线，分离出引擎适配与表示缩减导致的精确保真度损失。最后，我们对重建表面进行拓扑审计，证明仅显式连通性和共享索引不足以保证生产就绪资产，因为存在普遍的非流形结构、碎片化组件和边界伪影。总体而言，MeshSplatBench表明，可光栅性仅是基元级属性，而图形就绪性需要表示、拓扑和引擎兼容性的整体对齐。源代码将发布。

英文摘要

Triangle- and mesh-based neural rendering aims to bridge neural scene representations and existing graphics engines (\textit{e.g.}, Unity and Blender) by leveraging triangle primitives compatible with standard rasterization hardware. However, existing methods are developed and evaluated under inconsistent settings, with limited comparison and little investigation into practical graphics engine deployment. This gap significantly hinders the understanding of their real-world usability. To address this issue, we introduce MeshSplatBench, the first benchmark for systematic evaluation of triangle- and mesh-based neural rendering from native rendering to graphics engine deployment. We propose a hierarchical deployment protocol with two options: (1) Standard deployment, using a conventional opaque mesh pipeline with vertex colors and hardware Z-buffering; and (2) Dedicated deployment, incorporating method-specific engine implementations to preserve appearance and compositing properties (e.g., alpha blending). For mesh splatting, we further introduce a structural audit to evaluate the topological and geometric integrity of exported surfaces for downstream graphics applications. Extensive evaluations reveal three key findings: (1) graphics engine deployment introduces noticeable quality degradation across methods, while mesh splatting approaches achieve relatively better robustness under standard deployment; (2) dedicated deployment can preserve most rendering fidelity at the cost of approximately 6-30$\times$ slowdown; and (3) explicit connectivity and shared vertex indexing in current mesh splatting methods remain insufficient to guarantee manifoldness or global connectivity. Our benchmark demonstrates that rasterizability alone does not imply graphics readiness and highlights the importance of evaluating practical engine compatibility. The benchmark and source code will be publicly released.

发表机构

• Nanjing University of Science and Technology（南京理工大学）
• State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery（先进工程机械智能制造国家重点实验室）
• University of Surrey（萨里大学）

机构由 AI 辅助整理，请以论文原文为准。