RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving

By Yiming Zeng, Yu Hu, Shice Liu, Jing Ye, Yinhe Han, Xiaowei Li, Ninghui Sun. 2018

180 citations recorded by the source corpus.

For autonomous driving, vehicle detection is the prerequisite for many tasks like collision avoidance and path planning. In this letter, we present a real-time three-dimensional (RT3D) vehicle detection method that utilizes pure LiDAR point cloud to predict the location, orientation, and size of vehicles. In contrast to previous 3-D object detection methods, we used a pre-RoIpooling convolution technique that moves a majority of the convolution operations to ahead of the RoI pooling, leaving just a small part behind, so that significantly boosts the computation efficiency. We also propose a pose-sensitive feature map design which can be strongly activated by the relative poses of vehicles, leading to a high regression accuracy on the location, orientation, and size of vehicles. Experiments on the KITTI benchmark dataset show that the RT3D is not only able to deliver competitive detection accuracy against state-of-the-art methods, but also the first LiDAR-based 3-D vehicle detection work that completes detection within 0.09 s which is even shorter than the scan period of mainstream LiDAR sensors.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Andrew Markham
2. Sen Wang
3. R. Clark
4. Fei Liu
5. Michael A. Chapman
6. Tiago Cortinhal
7. Na Zhao
8. Gim Hee Lee

In Automotive Engineering

1. Fei Liu
2. Michael A. Chapman
3. Jonathan Li
4. Ying Li
5. Dongpu Cao
6. Jie Zhou
7. Qingyong Hu
8. Zilong Zhong

In Computer Vision and Pattern Recognition

1. Andrew Markham
2. Agathoniki Trigoni
3. Sen Wang
4. R. Clark
5. Xin Tan
6. Fei Liu
7. Michael A. Chapman
8. Xuequan Lu

Citations in the corpus

• Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection (2021, background): “…deep learning-based approaches (Li, Zhang, and Xia 2016; Chen et al. 2017; Beltrán et al. 2018; Yan, Mao, and Li 2018; Yang, Luo, and Urtasun 2018; Zeng et al. 2018; Zhou and Tuzel 2018; Chen et al. 2019; Lang et al. 2019; Qi et al. 2019; Shi, Wang, and Li 2019; Yang et al. 2019; Zhou et al.…”
• Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds (2019, background): “Object Detection The common way to detect objects in 3D point clouds is to project points onto 2D images to regress bounding boxes [25; 48; 3; 56; 59; 53].”
• FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds (2019, methodology): “The other group includes MV3D (LiDAR) [2], BirdNet [12], RT3D [13], VeloFCN [15] and LMNet [16] which are based on point clouds only.”
• Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review (2020, background): “The application of LiDAR point clouds for AVs can be concluded into three types: 3D point cloud segmentation, 3D object detection and localization, and 3D objects classification and recognition.”
• SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds (2020, background): “A common attempt to overcome the sparsity in LiDAR data is to project 3D point clouds into 2D image space either in the top-down [1,30] or spherical Range-View (RV) (i.”