A General Pipeline for 3D Detection of Vehicles
Name
1803.00387.pdf
Description
Submitted version
Size
3.46 MB
Format
Adobe PDF
Checksum (MD5)
1d33c2bc52b028dc1e5933eec64e4b0c
Author(s)
Du, Xinxin
Ang, Marcelo H.
Karaman, Sertac
Rus, Daniela
Date Issued
May 2018
Publisher
IEEE
Citation
Du, Xinxin, Ang, Marcelo H., Karaman, Sertac and Rus, Daniela. 2018. "A General Pipeline for 3D Detection of Vehicles."
Version
Original manuscript
Abstract
© 2018 IEEE. Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D detection networks. To identify the 3D box, an effective model fitting algorithm is developed based on generalised car models and score maps. A two-stage convolutional neural network (CNN) is proposed to refine the detected 3D box. This pipeline is tested on the KITTI dataset using two different 2D detection networks. The 3D detection results based on these two networks are similar, demonstrating the flexibility of the proposed pipeline. The results rank second among the 3D detection algorithms, indicating its competencies in 3D detection.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8461232
