HD　map　reconstruction　is　crucial　for　autonomous　driving.　LiDAR-based　methods　are　limited　due　to　expensive　sensors　and　time-consuming　computation.　Camera-based　methods　usually　need　to　perform　road　segmentation　and　view　transformation　separately,　which　often　causes　distortion　and　missing　content.　To　push　the　limits　of　the　technology,　we　present　a　novel　framework　that　reconstructs　a　local　map　formed　by　road　layout　and　vehicle　occupancy　in　the　bird＇s-eye　view　given　a　front-view　monocular　image　only.　We　propose　a　front-to-top　view　projection　(FTVP)　module,　which　takes　the　constraint　of　cycle　consistency　between　views　into　account　and　makes　full　use　of　their　correlation　to　strengthen　the　view　transformation　and　scene　understanding.　In　addition,　we　apply　multi-scale　FTVP　modules　to　propagate　the　rich　spatial　information　of　low-level　features　to　mitigate　spatial　deviation　of　the　predicted　object　location.　Experiments　on　public　benchmarks　show　that　our　method　achieves　various　tasks　on　road　layout　estimation,　vehicle　occupancy　estimation,　and　multi-class　semantic　estimation,　at　a　performance　level　comparable　to　the　state-of-the-arts,　while　maintaining　superior　efficiency.