用于逆过程幕墙建模的精细窗结构的半监督对抗识别
Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling
摘要：
深度学习方法是众所周知的数据饥渴，这需要大量的标记样本。不幸的是，大量的交互式样本标记工作极大地阻碍了深度学习方法的应用，特别是对于需要异构样本的3D建模任务。为了减轻立面学习3D建模的数据标注工作，提出了一种嵌入逆过程建模的半监督对抗识别策略。从纹理细节层次模型（LOD-2）出发，利用经典的卷积神经网络对图像块进行窗口类型识别和参数估计。然后将窗口类型和参数组装成过程语法。在现有的3D建模软件中构建了一个简单的程序引擎，生成细粒度的窗口几何形状。为了从少量标记样本中获得有用的模型，我们利用生成对抗网络以半监督的方式训练特征提取器。对抗训练策略还可以利用未标记的数据，使训练阶段更加稳定。使用公开的立面图像数据集的实验表明，所提出的训练策略可以获得约10%的分类精度和50%的改进，在相同的网络结构下的参数估计。此外，在针对具有不同外观样式的不可见数据进行测试时，性能提升更加明显。
英文摘要：
Deep learning methods are notoriously data-hungry, which requires a large number of labeled samples. Unfortunately, the large amount of interactive sample labeling efforts has dramatically hindered the application of deep learning methods, especially for 3D modeling tasks, which require heterogeneous samples. To alleviate the work of data annotation for learned 3D modeling of façades, this paper proposed a semi-supervised adversarial recognition strategy embedded in inverse procedural modeling. Beginning with textured LOD-2 (Level-of-Details) models, we use the classical convolutional neural networks to recognize the types and estimate the parameters of windows from image patches. The window types and parameters are then assembled into procedural grammar. A simple procedural engine is built inside an existing 3D modeling software, producing fine-grained window geometries. To obtain a useful model from a few labeled samples, we leverage the generative adversarial network to train the feature extractor in a semi-supervised manner. The adversarial training strategy can also exploit unlabeled data to make the training phase more stable. Experiments using publicly available façade image datasets reveal that the proposed training strategy can obtain about 10% improvement in classification accuracy and 50% improvement in parameter estimation under the same network structure. In addition, performance gains are more pronounced when testing against unseen data featuring different façade styles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
