Resource-Constrained Cellular Growth for Three-Dimensional Generation: A Developmental Framework for Emergent Geometry

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.

The 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.