Simultaneously　extracting　junctions　and　their　corresponding　line　segments　from　images　presents　a　promising　approach　to　structural　environment　cognition.　However,　conventional　methods　employ　square　convolution　for　line　feature　extraction,　resulting　in　the　exclusion　of　long-range　dependencies　and　the　generation　of　suboptimal　wireframe　predictions.　In　this　paper,　we　introduce　an　efficient　and　concise　parsing　method　named　Structural　Asymmetric　Convolution-based　Wireframe　Parser　(SACWP).　Taking　advantage　of　the　inherent　similarities　between　structural　asymmetric　convolution　and　the　predominant　distribution　of　line　segments　in　man-made　environments,　we　propose　a　Structural　Asymmetric　Convolution　module　(SAC)　that　captures　long-range　contextual　features　while　efficiently　filtering　out　irrelevant　information　from　neighboring　pixels.　Additionally,　we　introduce　a　feature　aggregation　module　based　on　dilated　convolution　(DCFA)　to　seamlessly　integrate　contextual　information　from　multiple　receptive　fields.　We　thoroughly　evaluate　our　approach　on　the　Wireframe　and　YorkUrban　datasets,　achieving　preferable　results　of　69.3%　and　29.7%　msAP　respectively.　On　the　other　hand,　the　promising　results　adequately　demonstrate　the　effectiveness　of　SACWP　to　Wireframe　Parsing　task.　©　2023　Elsevier　Ltd