Holistic Geometric Feature Learning for Structured Reconstruction
Ziqiong Lu Linxi Huan Qiyuan Ma Xianwei Zheng
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.