Generating 3D city models rapidly is crucial for many applications. Monocular height estimation is one of the most efficient and timely ways to obtain large-scale geometric information. However, existing works focus primarily on training and testing models using unbiased datasets, which don't align well with real-world applications. Therefore, we propose a new benchmark dataset to study the transferability of height estimation models in a cross-dataset setting. To this end, we first design and construct a large-scale benchmark dataset for cross-dataset transfer learning on the height estimation task. This benchmark dataset includes a newly proposed large-scale synthetic dataset, a newly collected real-world dataset, and four existing datasets from different cities. Next, two new experimental protocols, zero-shot and few-shot cross-dataset transfer, are designed. For few-shot cross-dataset transfer, we enhance the window-based Transformer with the proposed scale-deformable convolution module to handle the severe scale-variation problem. To improve the generalizability of deep models in the zero-shot cross-dataset setting, a max-normalization-based Transformer network is designed to decouple the relative height map from the absolute heights. Experimental results have demonstrated the effectiveness of the proposed methods in both the traditional and cross-dataset transfer settings. The datasets and codes are publicly available at https://thebenchmarkh.github.io/.
翻译:快速生成 3D 城市模型对于许多应用来说至关重要。 单高估计是获取大规模几何信息的最有效和及时的方法之一。 但是, 现有工作主要侧重于使用不偏倚的数据集来培训和测试模型, 这些数据集与现实世界应用程序不协调。 因此, 我们提出一个新的基准数据集, 用于研究跨数据集设置中高度估计模型的可转移性。 为此, 我们首先设计并构建一个大型基准数据集, 用于跨数据集传输学习关于高度估计任务的跨数据集。 这个基准数据集包括一个新的大型合成数据集、 新收集的真实世界数据集以及来自不同城市的4个现有数据集。 接下来, 设计了两个新的实验协议、 零点和点数点交叉数据集传输。 对于几个点的交叉数据集, 我们用拟议的可变缩放缩放模型加强基于窗口的变换变变变器, 处理严重的规模变换问题。 为了改进零点交叉数据集设置中的深模型的可概括性, 以最高标准为基础收集的真实数据集, 以及以最高标准为基础, 将量化的当前数据转换网络设计为标准。 将演示的绝对高度, 。 将演示的模型转换格式转换系统将测试系统 。