将NeRF论文自动转为可运行代码，几分钟完成人工数周工作。
NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code
- 用领域专属语法约束生成代码结构，确保符合NeRfStudio规范。
- 在30篇无公开代码的论文上实现接近专家水平的视觉质量（PSNR差值±0.5dB）。
- 适合研究者快速复现与改进复杂神经辐射场方法。
NeRF研究爆发式增长，但复现论文需大量手动编码。我们提出NERFIFY，一个面向NeRF领域的多智能体框架，能可靠地将论文转化为可训练的Nerfstudio插件，优于通用论文转代码方法及前沿大模型（如GPT-5）常失败的情况。通过六项创新：(1) 上下文无关语法（CFG）约束大模型输出，保证代码满足架构不变性；(2) 基于思维图的多文件智能体按依赖拓扑顺序生成代码库，逐节点验证契约与错误；(3) 组合引用恢复，自动从参考文献图中提取采样器、编码器等组件并集成；(4) 视觉反馈机制，通过PSNR最小区域分析、跨视角几何验证和视觉语言模型引导修复迭代优化质量；(5) 知识增强，支持在复现基础上引入新优化；(6) 构建基准评估体系，在30篇无公开实现的论文上，生成代码达到专家水平（PSNR±0.5dB，SSIM±0.2），实现时间从数周降至分钟级。结果表明，领域感知设计可有效支撑复杂视觉论文的代码转化，推动可复现研究的加速与普及。代码、数据与实现将公开发布。
原文摘要 · Abstract (English)
The proliferation of neural radiance field (NeRF) research requires significant efforts to reimplement papers before building upon them. We introduce NERFIFY, a multi-agent framework that reliably converts NeRF research papers into trainable Nerfstudio plugins, in contrast to generic paper-to-code methods and frontier models like GPT-5 that usually fail to produce runnable code. NERFIFY achieves domain-specific executability through six key innovations: (1) Context-free grammar (CFG): LLM synthesis is constrained by Nerfstudio formalized as a CFG, ensuring generated code satisfies architectural invariants. (2) Graph-of-Thought code synthesis: Specialized multi-file-agents generate repositories in topological dependency order, validating contracts and errors at each node. (3) Compositional citation recovery: Agents automatically retrieve and integrate components (samplers, encoders, proposal networks) from citation graphs of references. (4) Visual feedback: Artifacts are diagnosed through PSNR-minima ROI analysis, cross-view geometric validation, and VLM-guided patching to iteratively improve quality. (5) Knowledge enhancement: Beyond reproduction, methods can be improved with novel optimizations. (6) Benchmarking: An evaluation framework is designed for NeRF paper-to-code synthesis across 30 diverse papers. On papers without public implementations, NERFIFY achieves visual quality matching expert human code (+/-0.5 dB PSNR, +/-0.2 SSIM) while reducing implementation time from weeks to minutes. NERFIFY demonstrates that a domain-aware design enables code translation for complex vision papers, potentiating accelerated and democratized reproducible research. Code, data and implementations will be publicly released.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。