Precipitation-Driven 3D Generative Modeling: A Framework for Reaction-Guided Spatial Occupancy, Local Mineral Deposition, and Long-Term Shape Evolution
Abstract
Three-dimensional (3D) generative modeling has advanced through approaches such as 3D diffusion models, neural implicit representations, point-cloud generation, and Gaussian-based scene representations. However, many existing methods primarily formulate generation as the prediction, reconstruction, or refinement of geometric representations rather than as an explicit process in which material progressively occupies space through localized formation events. Inspired by precipitation and mineral deposition in natural environments, this paper proposes Precipitation-Driven 3D Generative Modeling (PD3GM), a conceptual framework that formulates 3D shape generation as a coupled process of chemical-state evolution, material formation, spatial deposition, and morphology-dependent growth. The framework represents the environment using a spatial field containing dissolved-material concentration, supersaturation, precipitation propensity, solid occupancy, and surface state. Local precipitation events convert available material into solid structures, while transport, surface attachment, geometric constraints, and optional dissolution govern subsequent evolution. A hybrid architecture combines process-based state updates with a learned proposal model, allowing the framework to generate shapes conditioned on target geometry, material properties, environmental conditions, or desired morphological characteristics. A voxel-based formulation, governing equations, training objectives, and a reference algorithm are presented. The framework is designed to support both physically motivated simulation and data-driven shape generation, including structures characterized by branching, layering, accumulation, and spatially heterogeneous growth. No experimental validation is claimed in this conceptual study. Its principal contribution is the proposed integration of precipitation-driven material formation with a general 3D generative architecture, together with a set of testable hypotheses and evaluation protocols for future implementation.
Related
- Collective Biological Construction: A Multi-Unit Generative Framework for Three-Dimensional Shape Formation Through Local Deposition and Collective Structural Evolution
- Layerwise Deposition-Field Generative Modeling: A Process-Conditioned Framework for Three-Dimensional Generation Through Thin-Film Deposition and Surface Growth
- Deposition-Based 3D Generative Modeling: A Framework for Three-Dimensional Object Generation Through Gas-Phase Particle Deposition, Local Adsorption, and Branching Growth
- Fluvial Erosion–Deposition Generative Model (FED-GM): A Process-Driven Framework for Three-Dimensional Terrain Generation Through Sediment Transport and Geomorphic Feedback
- Reaction-Driven Three-Dimensional Generative Computing: A Framework for Constructing 3D Objects Through Local Chemical Transformation, Deposition, and Structural Growth
BibTeX
@misc{zhang2026precipitationdriven,
title = {{Precipitation-Driven 3D Generative Modeling: A Framework for Reaction-Guided Spatial Occupancy, Local Mineral Deposition, and Long-Term Shape Evolution}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23635874},
url = {https://zenodo.org/records/23635874},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Precipitation-Driven 3D Generative Modeling: A Framework for Reaction-Guided Spatial Occupancy, Local Mineral Deposition, and Long-Term Shape Evolution
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/25
PB - Zenodo
DO - 10.5281/zenodo.23635874
UR - https://zenodo.org/records/23635874
AB - Three-dimensional (3D) generative modeling has advanced through approaches such as 3D diffusion models, neural implicit representations, point-cloud generation, and Gaussian-based scene representations. However, many existing methods primarily formulate generation as the prediction, reconstruction, or refinement of geometric representations rather than as an explicit process in which material progressively occupies space through localized formation events. Inspired by precipitation and mineral deposition in natural environments, this paper proposes Precipitation-Driven 3D Generative Modeling (PD3GM), a conceptual framework that formulates 3D shape generation as a coupled process of chemical-state evolution, material formation, spatial deposition, and morphology-dependent growth. The framework represents the environment using a spatial field containing dissolved-material concentration, supersaturation, precipitation propensity, solid occupancy, and surface state. Local precipitation events convert available material into solid structures, while transport, surface attachment, geometric constraints, and optional dissolution govern subsequent evolution. A hybrid architecture combines process-based state updates with a learned proposal model, allowing the framework to generate shapes conditioned on target geometry, material properties, environmental conditions, or desired morphological characteristics. A voxel-based formulation, governing equations, training objectives, and a reference algorithm are presented. The framework is designed to support both physically motivated simulation and data-driven shape generation, including structures characterized by branching, layering, accumulation, and spatially heterogeneous growth. No experimental validation is claimed in this conceptual study. Its principal contribution is the proposed integration of precipitation-driven material formation with a general 3D generative architecture, together with a set of testable hypotheses and evaluation protocols for future implementation.
ER -