MonoCD: Monocular 3D Object Detection with Complementary Depths MonoCD is a novel monocular 3D object detection method that improves depth estimation by introducing complementary depth. The method addresses the issue of depth prediction errors in existing multi-depth approaches, where errors tend to have the same sign, leading to reduced accuracy. To enhance complementarity, MonoCD introduces two key designs: (1) a new depth prediction branch that uses global and efficient depth clues from the entire image, reducing the similarity of depth predictions, and (2) the full exploitation of geometric relations between multiple depth clues to achieve complementarity in form. These designs enable higher complementarity in depth predictions, leading to improved overall accuracy. Experiments on the KITTI benchmark show that MonoCD achieves state-of-the-art performance without requiring additional data. The complementary depth module can also be used as a lightweight and plug-and-play component to enhance existing monocular 3D object detectors. The method is evaluated on the KITTI dataset, and results demonstrate that the complementary depth significantly improves detection accuracy, especially in terms of depth estimation. The method also shows good generalization across different datasets, including nuScenes. The approach is based on the CenterNet paradigm, with a focus on global and local depth clues. The method uses a feature extraction network and multiple prediction heads to generate depth predictions. The global clue branch predicts the horizon heatmap, while the local clue branch generates keypoint depths and direct depth. The complementary depth is derived from the global clue branch and is designed to complement the local depth predictions. The method also incorporates uncertainty modeling to weight the depth predictions and improve overall accuracy. The method is evaluated on the KITTI dataset, and results show that MonoCD outperforms existing methods in most metrics. The complementary depth significantly improves the accuracy of depth estimation, especially in terms of the BEV (Bird's Eye View) metric. The method is also tested on the nuScenes dataset, and results show that the method generalizes well across different datasets. The method is effective in reducing the coupling between depth predictions and improving overall detection accuracy. The method is also evaluated in terms of cross-dataset performance, showing that it is effective in different scenarios. The method is also tested in different configurations, showing that it is effective in various settings. The method is also evaluated in terms of different depth prediction branches, showing that it is effective in different scenarios. The method is also tested in different configurations, showing that it is effective in various settings. The method is also evaluated in terms of different depth prediction branches, showing that it is effective in different scenarios.MonoCD: Monocular 3D Object Detection with Complementary Depths MonoCD is a novel monocular 3D object detection method that improves depth estimation by introducing complementary depth. The method addresses the issue of depth prediction errors in existing multi-depth approaches, where errors tend to have the same sign, leading to reduced accuracy. To enhance complementarity, MonoCD introduces two key designs: (1) a new depth prediction branch that uses global and efficient depth clues from the entire image, reducing the similarity of depth predictions, and (2) the full exploitation of geometric relations between multiple depth clues to achieve complementarity in form. These designs enable higher complementarity in depth predictions, leading to improved overall accuracy. Experiments on the KITTI benchmark show that MonoCD achieves state-of-the-art performance without requiring additional data. The complementary depth module can also be used as a lightweight and plug-and-play component to enhance existing monocular 3D object detectors. The method is evaluated on the KITTI dataset, and results demonstrate that the complementary depth significantly improves detection accuracy, especially in terms of depth estimation. The method also shows good generalization across different datasets, including nuScenes. The approach is based on the CenterNet paradigm, with a focus on global and local depth clues. The method uses a feature extraction network and multiple prediction heads to generate depth predictions. The global clue branch predicts the horizon heatmap, while the local clue branch generates keypoint depths and direct depth. The complementary depth is derived from the global clue branch and is designed to complement the local depth predictions. The method also incorporates uncertainty modeling to weight the depth predictions and improve overall accuracy. The method is evaluated on the KITTI dataset, and results show that MonoCD outperforms existing methods in most metrics. The complementary depth significantly improves the accuracy of depth estimation, especially in terms of the BEV (Bird's Eye View) metric. The method is also tested on the nuScenes dataset, and results show that the method generalizes well across different datasets. The method is effective in reducing the coupling between depth predictions and improving overall detection accuracy. The method is also evaluated in terms of cross-dataset performance, showing that it is effective in different scenarios. The method is also tested in different configurations, showing that it is effective in various settings. The method is also evaluated in terms of different depth prediction branches, showing that it is effective in different scenarios. The method is also tested in different configurations, showing that it is effective in various settings. The method is also evaluated in terms of different depth prediction branches, showing that it is effective in different scenarios.