A modular and extensible library for parameterized terrain generation

2025-06-24

AI Summary

Existing terrain generation tools prioritize artistic expression and visual realism but lack parametric control, reproducibility, and scriptability—hindering their use in intelligent robotic simulation-driven development, where controllable and explicitly defined terrains are essential. To address this, we propose TerrainGen: a highly modular Python library for procedural terrain generation that integrates rule-based modeling with multi-scale noise synthesis. It enables fine-grained parameterization of physical attributes—including slope, surface roughness, and rock density—and adopts a loosely coupled architecture compatible with Blender for automated rendering and object placement. A declarative configuration interface further simplifies terrain specification. Experimental evaluation demonstrates TerrainGen’s effectiveness in synthetic data generation and perception ground-truth annotation, significantly improving controllability, reproducibility, and deployment efficiency in environment construction. By providing a scalable, programmable infrastructure, TerrainGen advances simulation-based robotics development and machine learning training pipelines.

Technology Category

• Intelligent Robots: Embodied AI
• Natural Language Processing: Generation
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Simulation-driven development of intelligent machines benefits from artificial terrains with controllable, well-defined characteristics. However, most existing tools for terrain generation focus on artist-driven workflows and visual realism, with limited support for parameterization, reproducibility, or scripting. We present a modular, Python-based library for procedural terrain generation that enables users to construct complex, parameterized terrains by chaining together simple modules. The system supports both structured and noise-based terrain elements, and integrates with Blender for rendering and object placement. The framework is designed to support applications such as generating synthetic terrains for training machine learning models or producing ground truth for perception tasks. By using a minimal but extensible set of modules, the system achieves high flexibility while remaining easy to configure and expand. We demonstrate that this enables fine-grained control over features such as slope, roughness, and the number of rocks, as well as extension to additional measures. This makes it well suited for workflows that demand reproducibility, variation, and integration with automated pipelines.

Problem

• Generates parameterized terrains for simulation-driven machine development
• Overcomes limited parameterization in existing artist-focused terrain tools
• Provides modular Python library for reproducible, scriptable terrain generation

Innovation

• Modular Python library for terrain generation
• Supports structured and noise-based elements
• Integrates with Blender for rendering