Multi-Task Multi-Sensor Fusion for 3D Object Detection

被引：575

作者：

Liang, Ming ^{[1
]}

Yang, Bin ^{[1
,2
]}

Chen, Yun ^{[1
,3
]}

Hu, Rui ^{[1
]}

Urtasun, Raquel ^{[1
,2
]}

机构：

[1] Uber Adv Technol Grp, Pittsburgh, PA 15201 USA

[2] Univ Toronto, Toronto, ON, Canada

[3] Uber AI Residency Program, San Francisco, CA USA

来源：

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) | 2019年

关键词：

D O I：

10.1109/CVPR.2019.00752

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. Our experiments show that all these tasks are complementary and help the network learn better representations by fusing information at various levels. Importantly, our approach leads the KITTI benchmark on 2D, 3D and bird's eye view object detection, while being real-time.

引用

页码：7337 / 7345

页数：9