Boosting Monocular 3D Object Detection via 2D Object Detection and Keypoint Estimation

Currently, monocular 3D object detection has attracted increasing attention due to its cost-effectiveness and scalability compared to LiDAR-based solutions or stereo-based solutions. However, the inherent absence of depth information in a single RGB image introduces significant uncertainty in 3D localization and pose estimation of objects. To address the ambiguity in estimating object dimensions and depth, many existing approaches rely on complex and heavy neural network architectures, which often hinder their practicality in real-world applications. This paper proposes a novel and lightweight multi-task framework for monocular 3D object detection, which leverages keypoint estimation and 2D object detection to infer 3D spatial attributes. Extensive experiments conducted on the KITTI benchmark demonstrate the efficiency and effectiveness of the proposed method. Notably, our model achieves an inference speed of up to 40 FPS, making it highly suitable for real-time deployment in intelligent driving scenarios.