🤖 AI Summary
This work addresses critical limitations in existing large language model (LLM)-based 3D scene generation methods for agricultural applications, including insufficient domain-specific knowledge, lack of validation mechanisms, and inadequate modularity, which collectively constrain controllability and scalability. To overcome these challenges, we propose a modular multi-LLM pipeline that integrates agricultural domain knowledge, few-shot prompting, retrieval-augmented generation (RAG), and Unreal Engine APIs to automatically construct realistic agricultural simulation environments. The architecture enables intermediate validation, structured data handling, and flexible extensibility, substantially enhancing semantic accuracy and visual fidelity. User studies and expert evaluations demonstrate that the system significantly outperforms manual design in both modeling efficiency and output quality, effectively overcoming the bottlenecks of conventional monolithic models in domain adaptation and controllable generation.
📝 Abstract
Procedural generation techniques in 3D rendering engines have revolutionized the creation of complex environments, reducing reliance on manual design. Recent approaches using Large Language Models (LLMs) for 3D scene generation show promise but often lack domain-specific reasoning, verification mechanisms, and modular design. These limitations lead to reduced control and poor scalability. This paper investigates the use of LLMs to generate agricultural synthetic simulation environments from natural language prompts, specifically to address the limitations of lacking domain-specific reasoning, verification mechanisms, and modular design. A modular multi-LLM pipeline was developed, integrating 3D asset retrieval, domain knowledge injection, and code generation for the Unreal rendering engine using its API. This results in a 3D environment with realistic planting layouts and environmental context, all based on the input prompt and the domain knowledge. To enhance accuracy and scalability, the system employs a hybrid strategy combining LLM optimization techniques such as few-shot prompting, Retrieval-Augmented Generation (RAG), finetuning, and validation. Unlike monolithic models, the modular architecture enables structured data handling, intermediate verification, and flexible expansion. The system was evaluated using structured prompts and semantic accuracy metrics. A user study assessed realism and familiarity against real-world images, while an expert comparison demonstrated significant time savings over manual scene design. The results confirm the effectiveness of multi-LLM pipelines in automating domain-specific 3D scene generation with improved reliability and precision. Future work will explore expanding the asset hierarchy, incorporating real-time generation, and adapting the pipeline to other simulation domains beyond agriculture.