DreamPrinting: Volumetric Printing Primitives for High-Fidelity 3D Printing

2025-03-02

AI Summary

Existing volumetric rendering techniques (e.g., NeRF) lack a differentiable, controllable mapping from radiance fields to physically printable translucent 3D structures—primarily due to the disconnect between learned radiation representations and real-world material properties (e.g., pigment compatibility, density, spectral optical response). DreamPrinting addresses this by introducing Volumetric Printing Primitives (VPPs): an explicit, material-centric voxel representation that unifies the Kubelka–Munk optical model with spectral calibration, enabling joint optimization of color, transmittance, and material density. Leveraging voxel-wise pigment concentration control, 3D stochastic halftoning, and explicit material encoding, VPPs establish an end-to-end differentiable mapping from radiance fields to print-ready voxels. Experiments demonstrate substantial improvements in internal structural consistency and optical fidelity for complex translucent objects—including hair, leaves, and clouds—outperforming conventional surface-based printing. Moreover, the framework natively supports integration with generative 3D models.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Transparent, Interpretable, Explainable ML
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Translating the rich visual fidelity of volumetric rendering techniques into physically realizable 3D prints remains an open challenge. We introduce DreamPrinting, a novel pipeline that transforms radiance-based volumetric representations into explicit, material-centric Volumetric Printing Primitives (VPPs). While volumetric rendering primitives (e.g., NeRF) excel at capturing intricate geometry and appearance, they lack the physical constraints necessary for real-world fabrication, such as pigment compatibility and material density. DreamPrinting addresses these challenges by integrating the Kubelka-Munk model with a spectrophotometric calibration process to characterize and mix pigments for accurate reproduction of color and translucency. The result is a continuous-to-discrete mapping that determines optimal pigment concentrations for each voxel, ensuring fidelity to both geometry and optical properties. A 3D stochastic halftoning procedure then converts these concentrations into printable labels, enabling fine-grained control over opacity, texture, and color gradients. Our evaluations show that DreamPrinting achieves exceptional detail in reproducing semi-transparent structures-such as fur, leaves, and clouds-while outperforming traditional surface-based methods in managing translucency and internal consistency. Furthermore, by seamlessly integrating VPPs with cutting-edge 3D generation techniques, DreamPrinting expands the potential for complex, high-quality volumetric prints, providing a robust framework for printing objects that closely mirror their digital origins.

Problem

• Transforming volumetric rendering into physical 3D prints.
• Ensuring pigment compatibility and material density in prints.
• Achieving high-fidelity color and translucency in 3D printing.

Innovation

• Transforms radiance-based volumetric representations into VPPs
• Integrates Kubelka-Munk model for pigment characterization
• Uses 3D stochastic halftoning for printable color gradients