3D Object Detection Algorithm with MixUp3D

Visual object detection is vital for autonomous vehicles and robot navigation. While 2D object detection has progressed significantly, 3D detection remains challenging. LiDAR-based methods are effective but expensive and impractical for widespread use, whereas cameras offer a costeffective alternative but lack comprehensive spatial information. This paper enhances 3D object detection from monocular images by modifying the SMOKE model with the MonoLSS architecture. Key improvements include the MixUp3D system and a visualization system, leading to better handling of complex situations. Experiments on the KITTI and nuScenes datasets demonstrate improved detection accuracy and efficiency.