A Survey for 3D Object Detection Algorithms from Images
- Authors
- 이한림; 김예지; 김병규
- Issue Date
- Sep-2022
- Publisher
- 한국멀티미디어학회
- Keywords
- 3D Object Detection; Autonomous Driving; Monocular Image Processing; Multi-View Image Processing; Stereo Image Processing.
- Citation
- Journal of Multimedia Information System, v.9, no.3, pp 183 - 190
- Pages
- 8
- Journal Title
- Journal of Multimedia Information System
- Volume
- 9
- Number
- 3
- Start Page
- 183
- End Page
- 190
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/152435
- DOI
- 10.33851/JMIS.2022.9.3.183
- ISSN
- 2383-7632
- Abstract
- Image-based 3D object detection is one of the important and difficult problems in autonomous driving and robotics, and aims to find and represent the location, dimension and orientation of the object of interest. It generates three dimensional (3D) bounding boxes with only 2D images obtained from cameras, so there is no need for devices that provide accurate depth information such as LiDAR or Radar. Image-based methods can be divided into three main categories: monocular, stereo, and multi-view 3D object detection. In this paper, we investigate the recent state-of-the-art models of the above three categories. In the multi-view 3D object detection, which appeared together with the release of the new benchmark datasets, NuScenes and Waymo, we discuss the differences from the existing monocular and stereo methods. Also, we analyze their performance and discuss the advantages and disadvantages of them. Finally, we conclude the remaining challenges and a future direction in this field.
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