2D object detection techniques have significant limitations when applied to automatic driving scenarios due to the ab-sence of description of the size,depth and other information of the physical environment.Numerous researchers have made exten-sive explorations in the field of image 3D object detection by aligning with the practical requirements of automatic driving.To conduct a comprehensive study in this domain,this paper reviews recent literature published both domestically and international-ly.It introduces two main categories of methods:image-based 3D object detection and 3D object detection by fusing image and point cloud data.Furthermore,it further subdivides these categories based on the different approaches used to process input data by the network.The paper describes representative methods within each category,summarizes the strengths and weaknesses of each method,and conducts a comparative analysis of their performance.Additionally,it provides a detailed introduction to relevant datasets and evaluation metrics for 3D object detection in autonomous driving scenarios.Finally,the paper analyzes the challenges and difficulties in the field of image 3D object detection,and outlines potential future research directions.
关键词
图像三维目标检测/深度学习/自动驾驶/多模态融合/计算机视觉
Key words
Image 3D object detection/Deep learning/Automatic driving/Multimodal fusion/Computer vision
维普
万方数据