Collective Biological Construction: A Multi-Unit Generative Framework for Three-Dimensional Shape Formation Through Local Deposition and Collective Structural Evolution
Abstract
Three-dimensional generative modeling has achieved substantial progress through neural implicit representations, point-based representations, volumetric methods, and diffusion-based generative models. However, an alternative modeling paradigm can be formulated by treating shape generation as a distributed construction process rather than as the direct prediction of a complete geometric object. Inspired by collective biological construction in coral colonies, this paper proposes Collective Biological Construction (CBC), a conceptual framework for three-dimensional generation in which multiple local construction units cooperate indirectly through neighborhood interactions, resource fields, deposition rules, and evolving structural constraints.
In CBC, the generated object emerges through repeated local operations. Each construction unit observes its local environment, estimates its growth potential, allocates available construction resources, deposits material, and modifies the conditions under which neighboring units subsequently operate. A shared structural state integrates local contributions into a globally connected three-dimensional object. The framework combines four principal mechanisms: multi-unit cooperation, localized material deposition, resource-mediated interaction, and collective structural evolution. Unlike a conventional one-shot geometry predictor, CBC explicitly represents the sequence of structural changes that produces the final object.
This paper formalizes CBC using a spatial state field, local construction agents, a resource-transport model, a deposition operator, and a connectivity-aware objective. It also describes a hybrid learning architecture in which neural policies can learn local construction decisions while explicit geometric operators maintain structural consistency. Potential applications include coral-like geometry synthesis, porous structures, branching architectures, procedural environment generation, and controllable three-dimensional asset creation. A reproducible evaluation protocol is proposed to compare CBC against procedural growth, agent-based construction, and learned three-dimensional generative baselines. Since the framework has not yet been empirically validated in this paper, its performance, computational advantages, and distinctiveness as a generative method remain hypotheses for future investigation.
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BibTeX
@misc{zhang2026collective,
title = {{Collective Biological Construction: A Multi-Unit Generative Framework for Three-Dimensional Shape Formation Through Local Deposition and Collective Structural Evolution}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23636054},
url = {https://zenodo.org/records/23636054},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Collective Biological Construction: A Multi-Unit Generative Framework for Three-Dimensional Shape Formation Through Local Deposition and Collective Structural Evolution
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/25
PB - Zenodo
DO - 10.5281/zenodo.23636054
UR - https://zenodo.org/records/23636054
AB - Three-dimensional generative modeling has achieved substantial progress through neural implicit representations, point-based representations, volumetric methods, and diffusion-based generative models. However, an alternative modeling paradigm can be formulated by treating shape generation as a distributed construction process rather than as the direct prediction of a complete geometric object. Inspired by collective biological construction in coral colonies, this paper proposes Collective Biological Construction (CBC), a conceptual framework for three-dimensional generation in which multiple local construction units cooperate indirectly through neighborhood interactions, resource fields, deposition rules, and evolving structural constraints. In CBC, the generated object emerges through repeated local operations. Each construction unit observes its local environment, estimates its growth potential, allocates available construction resources, deposits material, and modifies the conditions under which neighboring units subsequently operate. A shared structural state integrates local contributions into a globally connected three-dimensional object. The framework combines four principal mechanisms: multi-unit cooperation, localized material deposition, resource-mediated interaction, and collective structural evolution. Unlike a conventional one-shot geometry predictor, CBC explicitly represents the sequence of structural changes that produces the final object. This paper formalizes CBC using a spatial state field, local construction agents, a resource-transport model, a deposition operator, and a connectivity-aware objective. It also describes a hybrid learning architecture in which neural policies can learn local construction decisions while explicit geometric operators maintain structural consistency. Potential applications include coral-like geometry synthesis, porous structures, branching architectures, procedural environment generation, and controllable three-dimensional asset creation. A reproducible evaluation protocol is proposed to compare CBC against procedural growth, agent-based construction, and learned three-dimensional generative baselines. Since the framework has not yet been empirically validated in this paper, its performance, computational advantages, and distinctiveness as a generative method remain hypotheses for future investigation.
ER -