Three-dimensional　object　detection　is　an　essential　and　fundamental　task　in　the　field　of　computer　vision　which　can　be　widely　used　in　various　scenarios　such　as　autonomous　driving　and　visual　navigation.　In　view　of　the　current　insufficient　utilization　of　image　information　in　current　monocular　camera-based　3D　object　detection　algorithms,　we　propose　a　monocular　3D　object　detection　algorithm　based　on　pseudo-multimodal　information　extraction　and　keypoint　estimation.　We　utilize　the　original　image　to　generate　pseudo-lidar　and　a　bird’s-eye　view,　and　then　feed　the　fused　data　of　the　original　image　and　pseudo-lidar　to　the　keypoint-based　network　for　an　initial　3D　box　estimation,　finally　using　the　bird’s-eye　view　to　refine　the　initial　3D　box.　The　experimental　performance　of　our　method　exceeds　state-of-the-art　algorithms　under　the　evaluation　criteria　of　3D　object　detection　and　localization　on　the　KITTI　dataset,　achieving　the　best　experimental　performance　so　far.　©　2023　by　the　authors.