Multi-KeyPoint Depth Fusion for Monocular 3D Object Detection

Directly estimating the position and pose of objects in a 3D coordinate system from monocular images is an extremely challenging task. Many existing methods typically rely on direct regression or geometric projection techniques to obtain a single depth value for the target, but these approaches often struggle to provide sufficiently accurate depth estimates in complex scenes. To address this issue, we propose an improved 3D object detection method that enhances depth estimation accuracy by fusing depth information from multiple keypoints. Specifically, the method first predicts multiple corner points and surface center points of the 3D bounding box. Then, based on the image heights and physical heights of these keypoints, multiple depth values are computed through geometric projection. These depth values are subsequently fused with the depth information directly regressed by the depth branch to achieve more reliable depth estimation. Additionally, to improve the model's capability in detecting objects of varying scales, we introduce a multi-scale attention enhancement module into the feature extraction network. Finally, experimental results on the KITTI dataset demonstrate that the proposed method significantly improves the performance of 3D object detection.