{
  "abstract": "Contemporary three-dimensional (3D) generative methods commonly construct objects through learned geometric representations, probabilistic denoising, implicit fields, point-based primitives, or volumetric decoding. This paper proposes Resource-Constrained Cellular Growth (RCCG), a developmental framework for 3D generation in which object geometry emerges through repeated local growth operations rather than being produced exclusively as a final geometric prediction. RCCG represents a developing object as a dynamically evolving population of spatially organized cells. Each cell maintains a developmental state, resource demand, local neighborhood, differentiation potential, and a set of admissible growth actions. Cell division, elongation, differentiation, branching, fusion, and termination are controlled by local rules, environmental signals, geometric constraints, and a finite resource budget. A hierarchical growth graph connects cellular events to tissue-level structures, while a continuous geometric decoder converts the resulting developmental state into a surface, volumetric representation, point cloud, or Gaussian primitive set.\n\nThe proposed framework formalizes growth as a constrained dynamical process and introduces objectives for target-shape agreement, resource efficiency, structural continuity, collision avoidance, developmental stability, and topological plausibility. It is intended for applications involving branching structures, plant-like objects, root systems, coral-like forms, and selected tissue-inspired geometries. Unlike conventional L-systems or cellular automata considered in isolation, RCCG is designed to integrate local developmental rules, explicit resource allocation, adaptive branching, and multiple output representations within a unified generation pipeline. This paper defines the model and its evaluation protocol but does not claim completed implementation or empirical validation. Its contribution is therefore a testable research proposal whose novelty and performance must be established through comparison with existing developmental modeling, procedural generation, and neural generative methods.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Jincheng Zhang",
      "orcid": "0009-0005-1860-0009"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bv5zynvc2qxmj.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23636035",
  "fulltext_url": null,
  "id": "bv5zynvc2qxmj",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23636035",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23636035/files/Resource-Constrained%20Cellular%20Growth%20for%20Three-Dimensional%20Generation.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bv5zynvc2qxmj.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:21:50Z",
      "native_id": "23636035",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23636035"
    }
  ],
  "subjects": [],
  "title": "Resource-Constrained Cellular Growth for Three-Dimensional Generation: A Developmental Framework for Emergent Geometry",
  "title_text": "Resource-Constrained Cellular Growth for Three-Dimensional Generation: A Developmental Framework for Emergent Geometry",
  "type": "preprint",
  "updated_at": "2026-11-08T12:21:50Z",
  "url": "https://research.draconitic.com/paper/bv5zynvc2qxmj",
  "venue": "Zenodo",
  "versions": []
}