MoPoSD: monocular 3D object detector with potential for specialized exploration in depth

Monocular 3D object detection is a critical task in autonomous driving and robotic vision. Recently, center point-based monocular 3D object detection methods have achieved a balance between speed and accuracy. However, these methods face challenges in depth information acquisition and performance potential, often relying heavily on local information. To address these challenges, we propose a novel monocular 3D object detection model named MoPoSD (Monocular 3D Object Detector with Potential for Specialized Exploration in Depth). MoPoSD utilizes an attention mechanism tailored for object detection and incorporates an encoder-decoder architecture. Our model introduces an efficient encoder, TIE (Targeted Interaction Encoder), which updates features in an interlaced manner to achieve lightweight processing. Additionally, we propose a branch module for depth information estimation called Depth Attention Fusion Module). This module leverages multi-scale feature layer pyramid fusion and attention mechanism to improve depth estimation accuracy and the model's robustness when handling objects at varying distances. Furthermore, our proposed decoder also introduces an advanced group-decision mechanism to enhance the supervision signal of the training target, named MSMD (Multiple Sets of Matching Decoders). Extensive experiments demonstrate that MoPoSD achieves state-of-the-art performance on the KITTI benchmark using monocular images as input without requiring additional dense depth annotations.