Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Wallin, Erik
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414684698
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