Coupled Physics-Gated Adaptation: Spatially Decoding Volumetric Photochemical Conversion in Complex 3D-Printed Objects

TL;DR

This paper introduces a novel multimodal deep learning framework, C-PGA, that predicts volumetric photochemical conversion in complex 3D-printed objects by modeling physical interactions and visual data, enabling virtual chemical characterization.

Contribution

The paper presents C-PGA, a new architecture that explicitly models physical coupling in 3D vision tasks, using sparse parameters to dynamically gate visual features for predicting chemical states.

Findings

Achieved accurate prediction of chemical conversion in complex 3D prints.

Demonstrated the effectiveness of physics-guided feature modulation.

Enabled virtual chemical characterization without post-print measurements.

Abstract

We present a framework that pioneers the prediction of photochemical conversion in complex three-dimensionally printed objects, introducing a challenging new computer vision task: predicting dense, non-visual volumetric physical properties from 3D visual data. This approach leverages the largest-ever optically printed 3D specimen dataset, comprising a large family of parametrically designed complex minimal surface structures that have undergone terminal chemical characterisation. Conventional vision models are ill-equipped for this task, as they lack an inductive bias for the coupled, non-linear interactions of optical physics (diffraction, absorption) and material physics (diffusion, convection) that govern the final chemical state. To address this, we propose Coupled Physics-Gated Adaptation (C-PGA), a novel multimodal fusion architecture. Unlike standard concatenation, C-PGA explicitly models physical coupling by using sparse geometrical and process parameters (e.g., surface transport, print layer height) as a Query to dynamically gate and adapt the dense visual features via feature-wise linear modulation (FiLM). This mechanism spatially modulates dual 3D visual streams-extracted by parallel 3D-CNNs processing raw projection stacks and their diffusion-diffraction corrected counterparts allowing the model to recalibrate its visual perception based on the physical context. This approach offers a breakthrough in virtual chemical characterisation, eliminating the need for traditional post-print measurements and enabling precise control over the chemical conversion state.