3D synthesis and visualization of porous structures

Abstract

Porous materials are ubiquitous in nature and are widely used in many applications. However, there is still a lack of computational methods for synthesizing and modeling the natural complex porous structures. The example-based texture synthesis methods are known for synthesizing images that greatly mimic the input examplar. Inspired by this, we thus extend it to the synthesis of 3d porous structures. We present the first framework that can synthesize porous material that is structurally consistent to input 3D exemplar. In our framework, the 2D texture optimization method is extended built upon 3D neighborhood. An adaptive weighted mechanism method is proposed to reduce blurring and accelerate the convergence speed. Moreover, a connectivity pruning algorithm is performed as post-processing to prune spurious branches. Experimental results demonstrate that our method can preserve both the structural continuity and material descriptors while maintaining visual similarity with input.

The visual analysis of porous structures is difficult and significant in many scientific research domains, such as fluid flow in porous formations. We present a visualization approach for the analysis of CO2 bubble-induced attenuation in porous rock formations for solving domain research problems. As a basis for this, we introduce customized techniques to extract CO2 bubbles and their surrounding porous structure from XRCT measurements. To understand how the structure of porous media influences the occurrence and shape of formed bubbles, we automatically classify and relate them in terms of morphology and geometric features, and further directly support searching for promising porous structures. To allow for the meaningful direct visual comparison of bubbles and their structures, we propose a customized registration technique considering the bubble shape as well as its points of contact to the porous media surface. With our quantitative extraction of geometric bubble features, we further support the analysis as well as the creation of a physical model. We demonstrate that our approach was successfully used to answer several research questions in the domain, and discuss its high practical relevance to identify critical seismic characteristics of fluid-saturated rock that govern its capability to store CO2.

In recent years, deep learning techniques have overwhelmingly changed the way of solving computer vision and graphics tasks. The methods on 3D shape generation have gained great performance improvement, which shows good generated results on 3d man-made models with semantic, regular and repetitive shapes. However, these methods focus on generating the 3D surface appearance without considering internal structures. Thus, we present 3D-axisGAN, a novel 3D axis-attention enhanced generative network, to synthesize 3D shape with complex structures that are strictly similar to the real exemplar in both visual, geometry, and statistical senses. A novel 3D-axis attention module is proposed to incorporate into each sub-net, which effectively enhances the intermediate feature maps with attention computation from x, y, and z-axis respectively. A comparison study demonstrates that our network outperforms state-of-the-art methods.