Digitally synthesizing scaffold-like materials with complex structures, e.g., bones or metal foam, is a fundamental yet challenging task in tissue engineering and other biomedical applications, because it is difficult to generate synthesized results with equal visual complexity, strong spatial coherence, and similar statistical metrics. To handle these challenges, we present ScaffoldGAN, an efficient end-to-end framework based on generative adversarial networks (GANs) for synthesizing three-dimensional (3D) materials with complex internal structures resembling the given exemplar. Specifically, we propose a novel structural loss to enforce strong spatial coherence in the synthesized results by leveraging the deep features learned by our networks. To demonstrate the effectiveness of our model and the proposed structural loss term, we collected example data containing various structural complexities, covering two categories of materials, i.e., bones and metal foams. Extensive comparative experiments on these collected data showed that our method outperforms state-of-the-art methods, producing synthesized results with better visual quality and desirable statistical metrics. The ablation study proves the structural loss is the main contributor to the performance gain, validating our design choice.

数字合成具有复杂结构（例如骨骼或金属泡沫）的类似支架的材料，是组织工程和其他生物医学应用中一项基本但具有挑战性的任务，因为很难产生具有相同视觉复杂性，强大的空间连贯性和类似特性的合成结果统计指标。为了应对这些挑战，我们提出了ScaffoldGAN，这是一个基于生成对抗网络（GAN）的高效端到端框架，用于合成具有类似于给定示例的复杂内部结构的三维（3D）材料。具体而言，我们提出了一种新颖的结构损失，以利用我们的网络学习到的深层特征，在合成结果中强制实现强大的空间连贯性。为了证明我们的模型和建议的结构损失项的有效性，我们收集了包含各种结构复杂性的示例数据，涵盖了两类材料，即骨骼和金属泡沫。对这些收集的数据进行的广泛比较实验表明，我们的方法优于最新方法，可产生具有更好视觉质量和理想统计指标的合成结果。烧蚀研究证明结构损失是性能提高的主要因素，从而验证了我们的设计选择。