MeshSplatBench: A Unified Benchmark for Triangle-Based Neural Rendering
Kaixuan Zhang, Minxian Li, Mingwu Ren, Xiatian Zhu
Abstract
Triangle-based neural rendering bridges neural scene representations and conventional graphics pipelines by optimizing explicit geometric primitives compatible with standard rasterization hardware. However, existing approaches are evaluated almost exclusively within custom research renderers, obscuring their practical deployability in production engines. To bridge this gap, we introduce \textbf{MeshSplatBench}, a unified benchmark that systematically investigates triangle-based neural rendering across the complete pipeline from native optimization to game-engine deployment. MeshSplatBench establishes a standardized evaluation protocol while preserving each method's native optimization semantics, reproducing published results within $0.8\%$ PSNR deviation. Furthermore, we introduce a hierarchical Unity deployment protocol spanning three rendering tiers: native CUDA renderers, method-specific dedicated engine shaders, and standard opaque mesh pipelines, isolating the exact fidelity losses caused by engine adaptation \textit{vs.} representation reduction. Finally, we conduct a topological audit of reconstructed surfaces, demonstrating that explicit connectivity and shared indexing alone are insufficient to guarantee production-ready assets due to prevalent non-manifold structures, fragmented components, and boundary artifacts. Overall, MeshSplatBench demonstrates that rasterizability is merely a primitive-level attribute, whereas graphics readiness requires jthe holistic alignment of representation, topology, and engine compatibility. Source code will be released.