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RTM3D: Real-Time Monocular 3D Detection from Object Keypoints for Autonomous Driving
Li PX(李培玄)1,2,3,4,5; Zhao HC(赵怀慈)1,2,4,5; Liu PF(刘鹏飞)1,2,3,4,5; Cao FD(曹飞道)1,2,3,4,5
Department光电信息技术研究室
Conference Name2020 European Conference on Computer Vision (ECCV 2020)
Conference DateAugust 23-28, 2020
Conference PlaceGlasgow, UK
Source Publication2020 European Conference on Computer Vision (ECCV 2020)
PublisherSpringer Science and Business Media Deutschland GmbH
Publication PlaceBerlin
2020
Pages644-660
Contribution Rank1
ISBN978-3-030-58580-8
KeywordReal-time monocular 3D detection Autonomous driving Keypoint detection
AbstractIn this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without any extra annotations, category-specific 3D shape priors, or depth maps. Our method is the first real-time system (FPS > 24) for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/27957
Collection光电信息技术研究室
Corresponding AuthorZhao HC(赵怀慈)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
3.University of Chinese Academy of Sciences, Beijing, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China
5.Key Lab of Image Understanding and Computer Vision, Shenyang, China
Recommended Citation
GB/T 7714
Li PX,Zhao HC,Liu PF,et al. RTM3D: Real-Time Monocular 3D Detection from Object Keypoints for Autonomous Driving[C]. Berlin:Springer Science and Business Media Deutschland GmbH,2020:644-660.
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