The inference of topological principles is a key problem in structured reconstruction. We observe that wrongly predicted topological relationships are often incurred by the lack of holistic geometry clues in low-level features. Inspired by the fact that massive signals can be compactly described with frequency analysis, we experimentally explore the efficiency and tendency of learning structure geometry in the frequency domain. Accordingly, we propose a frequency-domain feature learning strategy (F-Learn) to fuse scattered geometric fragments holistically for topology-intact structure reasoning. Benefiting from the parsimonious design, the F-Learn strategy can be easily deployed into a deep reconstructor with a lightweight model modification. Experiments demonstrate that the F-Learn strategy can effectively introduce structure awareness into geometric primitive detection and topology inference, bringing significant performance improvement to final structured reconstruction. Code and pre-trained models are available at https://github.com/Geo-Tell/F-Learn.

翻译：拓扑原理推断是结构化重建中的一个关键问题。我们观察到，错误的拓扑关系预测通常源于低级特征中缺乏整体几何线索。受大量信号可通过频率分析实现紧凑描述这一事实启发，我们实验性地探索了在频域中学习结构几何的效率与倾向性。据此，我们提出了一种频域特征学习策略（F-Learn），用于将分散的几何片段整体融合以实现结构完整的拓扑推理。得益于其简洁设计，F-Learn 策略可轻松部署到深度重建器中，仅需轻量级模型修改。实验表明，F-Learn 策略能够有效将结构感知引入几何基元检测与拓扑推断，为最终的结构化重建带来显著的性能提升。代码与预训练模型已发布于 https://github.com/Geo-Tell/F-Learn。