A modular and extensible library for parameterized terrain generation (2506.19751v1)

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.

• The paper presents a modular, parameterized library that chains simple processing modules to generate synthetic terrains with explicit control over features like slope, roughness, and rock density.
• It demonstrates precise parameter tuning using analytical proxies and empirical validation to achieve target metrics, ensuring predictable modifications during terrain synthesis.
• The integration with Blender facilitates high-fidelity rendering and dataset generation, streamlining simulation-to-real workflows for robotics and machine learning research.

Overview of "A modular and extensible library for parameterized terrain generation"

This work presents a Python-based library designed for procedural, parameterized terrain generation with an emphasis on reproducibility, scriptability, and integration into simulation-driven research workflows. The approach distinctly prioritizes control over specific terrain parameters over purely visual realism, addressing a gap in existing artist-oriented terrain generation tools by offering explicit, modular parameterization of features critical for robotics, autonomous vehicles, and machine learning applications requiring synthetic environments.

Architecture and Modularity

The core of the system is a pipeline of composable modules, implemented analogously to UNIX pipes: simple, single-purpose operators are sequentially chained, where each module processes input data (primarily 2D heightmaps) and produces processed output for downstream modules. Two principal lists are maintained (temporary and primary) to manage and stage terrains during the pipeline, akin to the design pattern found in image processing suites like ImageMagick.

The modules are divided into several functional categories:

• Generation: Producing elementary terrains using noise (Simplex, Perlin), analytical functions (Gaussian, Plane, Donut, etc.), or compositing obstacles (e.g., rocks, holes).
• Combination: Merging terrain layers using weighted sums or operations such as addition, multiplication, min, and max.
• Modification/Analysis: Adjusting terrain with operations such as smoothing, scaling, clipping, and computing analytical metrics like slope, roughness, and rock counts.
• Integration and Visualization: Saving/loading terrains, rendering via Blender integration, and generating segmentation masks, depth maps, and textured visualizations for downstream ML pipelines.

Modules are designed as iterable Python objects with callable interfaces, enabling straightforward extension and easy implementation of new processing steps.

Parameterization and Control

A critical strength of the library is its explicit support for parameterized terrain generation. Parameters—such as slope, roughness, and rock density—are set via:

• Sampling from (possibly user-defined) statistical distributions
• Proxy-based weighting strategies for compositional modules (e.g., WeightedSum over base terrain elements), where theoretical proxies for aggregate features (like roughness and slope) are derived analytically or empirically
• Control-flow primitives (such as Loop, EndLoop) that automate sweeping over parameter spaces or create composite terrains by tessellating over a large domain with seamless random seed management

Obstacles (e.g., rocks) are parameterized through distributions over placement, size, and elevation; their spatial statistics are empirically validated and align well with established terrain classification schemas (e.g., those used in Swedish forestry industry practices).

The entire workflow is scriptable from the command-line or YAML configuration files, enabling guaranteed reproducibility and straightforward integration with automated simulation/ML pipelines.

Numerical Results and Empirical Validation

The paper provides extensive ablation and demonstration on parameterized control:

• The system can precisely control output metrics such as surface slope (via gradient analysis), roughness (surface area ratios post-smoothing), and rock count/distribution.
• Proxy-based weighting adjustments in compositional modules support close approximation of target aggregate metrics. For example, the target mean slope $s$ is achieved by solving for weights $w_i$ such that $\bar{\nabla} z = \sum w_i \bar{\nabla} z_i$ matches the specified value.
• Empirical evaluation across large sweeps of parameter space confirms monotonic and predictable relationships between module settings (number of octaves, scale, amplitude, persistence in noise modules) and surface characteristics.
• The system supports batch terrain generation and direct creation of meta-data and segmentation masks suitable for supervised learning and simulation-to-real data transfer.

Blender Integration and Downstream Application

The direct integration with Blender augments the system for high-fidelity rendering and simulation. Features include:

• Automated asset creation: raster-to-mesh conversion, top-down or arbitrary camera viewpoints, texturing with both terrain metrics and analytic overlays.
• Insertion of mesh obstacles for embedded object simulation, segmentation mask generation, and depth image synthesis.
• Reproducibility for visual assets; exact command invocations (available in the appendix) result in deterministic datasets.

These capabilities are particularly suited for machine learning dataset generation (e.g., for training segmentation networks or evaluating perception algorithms) and virtual experimentation in robotics simulation.

Limitations and Design Trade-offs

The framework fundamentally represents terrains as 2D heightfields, excluding overhangs and volumetric underground features. Although Blender allows embedding arbitrary geometry, mesh-object management is currently focused on embedding rather than fully modeling complex solids.

The command/script-driven interface optimizes for reproducibility, versioning, and automation but may initially present a steeper learning curve compared to GUI-based, artist-centric tools. However, the structure is amenable to future visual-programming and web-based front-ends.

Implications and Future Directions

Practical Implications

• The library provides a reproducible, extensible backbone for synthetic terrain and dataset generation in simulation, facilitating advances in sim-to-real transfer, sensor data augmentation, and physically grounded robotics ML development.
• Explicit, scriptable parameterization of environment features enables controlled experimentation and benchmarking that was previously infeasible with more monolithic, artist-driven tools.
• The design allows for rapid prototyping and sharing of complete experiment configurations, supporting open and collaborative research.

Theoretical Implications

• The proxy-based approach to parameter aggregation, where per-layer metrics can be analytically composed (or accurately approximated) into whole-terrain metrics, offers a scalable mechanism for controlled synthetic dataset creation. This has potential to influence benchmark dataset construction and experimental standardization.

Prospective Developments

• Extension to richer terrain representations: direct volumetric or mesh-based modules for overhanging and non-heightfield topologies.
• Deeper Blender integration: procedural asset placement, richer annotation (instance segmentation, material decomposition), and animation scripting.
• Intuitive web-based or visual programming interfaces to further democratize usage.
• Coupling with procedural vegetation, water flow, or erosion modelling, extending applicability to environmental science and geospatial simulation.

Conclusion

The presented library bridges a significant gap in synthetic terrain generation by providing a highly modular, parameterized, and scriptable framework suited for simulation-centered research and ML dataset creation. Its architecture underpins reproducibility and extensibility, addressing the explicit needs of intelligent machinery and perception research workflows. The source code availability under an open license and reproducibility of results further enhance its value as a research infrastructure component.

Key contributions include:

• Proxy-based, analytically-informed parameterization of compositional terrains
• Empirical validation of module settings on terrain features
• Seamless integration with Blender for dataset-ready rendering and segmentation
• A compact but highly extensible system design

The approach lays a solid foundation for further development in procedural simulation environments, serving as both a practical tool and a reference framework for parameterized, synthetic data creation.