自动驾驶场景下的图像三维目标检测研究进展

doi:10.11896/jsjkx.231000075

Abstract

Abstract: 2D object detection techniques have significant limitations when applied to automatic driving scenarios due to the absence of description of the size,depth and other information of the physical environment.Numerous researchers have made extensive 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 internationally.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.

Key words: Image 3D object detection, Deep learning, Automatic driving, Multimodal fusion, Computer vision

CLC Number:

TP391

ZHOU Yan, XU Yewen, PU Lei, XU Xuemiao, LIU Xiangyu, ZHOU Yuexia. Research Progress of Image 3D Object Detection in Autonomous Driving Scenario[J].Computer Science, 2024, 51(11): 133-147.

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