Planar　3D　reconstruction　aims　to　simultaneously　extract　plane　instances　and　reconstruct　the　local　3D　model　through　the　estimated　plane　parameters.　Existing　methods　achieve　promising　results　either　through　selfattention　or　convolution　neural　network　(CNNs),　but　usually　ignore　the　complementary　properties　of　them.　In　this　paper,　we　propose　a　line　-guided　planar　3D　reconstruction　method　PlaneAC,　which　leverages　the　advantage　of　self　-attention　and　CNNs　to　capture　long-range　dependencies　and　alleviate　the　computational　burden.　In　addition,　explicit　connection　between　two　adjacent　attention　layers　is　built　for　better　leveraging　the　transferable　knowledge　and　facilitating　the　information　flow　between　tokenized　feature　from　different　layers.　Therefore,　the　subsequent　attention　layer　can　directly　interact　with　previous　results.　Finally,　a　line　segment　filtering　method　is　presented　to　remove　irrelevant　guiding　information　from　indistinctive　line　segments　extracted　from　the　image.　Extensive　experiments　on　ScanNet　and　NYUv2　public　datasets　demonstrate　the　preferable　performance　of　our　proposed　method,　and　the　results　show　that　PlaneAC　achieves　a　better　trade-off　between　accuracy　and　computation　cost　compared　with　other　state-of-the-art　methods.