arXiv subjects

Diogo de Andrade

Publications and source records attributed to Diogo de Andrade.

4 recordsLinked to original sources

arXiv2025

Structural Tree Extraction from 3D Surfaces

This paper introduces a method to extract a hierarchical tree representation from 3D unorganized polygonal data. The proposed approach first extracts a graph representation of the surface, which serves as the foundation for structural analysis. A Steiner tree is then generated to establish an optimized connection between key terminal points, defined according to application-specific criteria. The structure can be further refined by leveraging line-of-sight constraints, reducing redundancy while preserving essential connectivity. Unlike traditional skeletonization techniques, which often assume volumetric interpretations, this method operates directly on the surface, ensuring that the resulting representation remains relevant for navigation-aware geometric analysis. The method is validated through two use cases: extracting structural representations from tile-based elements for procedural content generation, and identifying key points and structural metrics for automated level analysis. Results demonstrate its ability to produce simplified, coherent representations, supporting applications in procedural generation, spatial reasoning, and map analysis.

cs.GR↗

arXiv2025

Generating 3D Terrain with 2D Cellular Automata

This paper explores the use of 2D cellular automata (CA) to generate 3D terrains through a simple additive approach. Experimenting with multiple CA transition rules produced aesthetically interesting, navigable landscapes, suggesting applicability for terrain generation in games.

nlin.CG↗

arXiv2024

Generating Multidimensional Clusters With Support Lines

Synthetic data is essential for assessing clustering techniques, complementing and extending real data, and allowing for more complete coverage of a given problem's space. In turn, synthetic data generators have the potential of creating vast amounts of data -- a crucial activity when real-world data is at premium -- while providing a well-understood generation procedure and an interpretable instrument for methodically investigating cluster analysis algorithms. Here, we present Clugen, a modular procedure for synthetic data generation, capable of creating multidimensional clusters supported by line segments using arbitrary distributions. Clugen is open source, comprehensively unit tested and documented, and is available for the Python, R, Julia, and MATLAB/Octave ecosystems. We demonstrate that our proposal can produce rich and varied results in various dimensions, is fit for use in the assessment of clustering algorithms, and has the potential to be a widely used framework in diverse clustering-related research tasks.

cs.LG↗

arXiv2022

Procedural Generation of 3D Maps with Snappable Meshes

In this paper we present a technique for procedurally generating 3D maps using a set of premade meshes which snap together based on designer-specified visual constraints. The proposed approach avoids size and layout limitations, offering the designer control over the look and feel of the generated maps, as well as immediate feedback on a given map's navigability. A prototype implementation of the method, developed in the Unity game engine, is discussed, and a number of case studies are analyzed. These include a multiplayer game where the method was used, together with a number of illustrative examples which highlight various parameterizations and piece selection methods. The technique can be used as a designer-centric map composition method and/or as a prototyping system in 3D level design, opening the door for quality map and level creation in a fraction of the time of a fully human-based approach.

cs.AI↗