Multi-scale Structural Asymmetric Convolution for Wireframe Parsing

Extracting salient line segments with their corresponding junctions is a promising method for structural environment recognition. However, conventional methods extract these structural features using square convolution, which greatly restricts the model performance and leads to unthoughtful wireframes due to the incompatible geometric properties with these primitives. In this paper, we propose a Multi-scale Structural Asymmetric Convolution for Wireframe Parsing (MSACWP) to simultaneously infer prominent junctions and line segments from images. Benefiting from the similar geometric properties of asymmetric convolution and line segment, the proposed Multi-Scale Asymmetric Convolution (MSAC) effectively captures long-range context feature and prevents the irrelevant information from adjacent pixels. Besides, feature maps obtained from different stages in decoder layers are combined using Multi-Scale Feature Combination module (MSFC) to promote the multi-scale feature representation capacity of the backbone network. Sufficient experiments on two public datasets (Wireframe and YorkUrban) are conducted to demonstrate the advantages of our proposed MSACWP compared with previous state-of-the-art methods.