Visual Permeability-Driven Generation of Support-Free Stochastic Porous Structures

While stochastic porous structures are prized for their aesthetics and lightweight properties, they remain challenging to create and manufacture. Key challenges include design difficulty, manufacturing constraints due to internal supports, and a lack of metrics to evaluate visual permeability (VP). We propose a three-stage generation and optimization framework to bridge the gap between procedural design and digital fabrication. First, a novel quantitative VP metric is used to drive a graph-based connectivity optimization process that maximizes aesthetic transparency and structural sparsity. Second, orthotropic Gaussian-kernel implicit fields are used to model the continuous stochastic porous geometry, intrinsically reducing initial overhangs. Finally, a density-field optimization, constrained by a differentiable layer-wise additive manufacturing (AM) filter, is applied to enforce support-free manufacturability with minimal geometric distortion. Our method facilitates the creation of complex, highly permeable porous structures that can be seamlessly fabricated without the need for additional support.

We thank all the anonymous reviewers for their valuable comments and constructive suggestions. This work is supported by the grant No.61972232 from National Nat- ural Science Foundation of China (NSFC) and the Key Research and Development Plan of Shandong Province of China (No.2020ZLYS01).