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Cited by 593

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Stereo R-CNN Based 3D Object Detection for Autonomous Driving

Open access
Li, Peiliang, Chen, Xiaozhi, Shen, Shaojie·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2019.00783
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Abstract

We propose a 3D object detection method for autonomous driving by fully exploiting the sparse and dense, semantic and geometry information in stereo imagery. Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect and associate object in left and right images. We add extra branches after stereo Region Proposal Network (RPN) to predict sparse keypoints, viewpoints, and object dimensions, which are combined with 2D left-right boxes to calculate a coarse 3D object bounding box. We then recover the accurate 3D bounding box by a region-based photometric alignment using left and right RoIs. Our method does not require depth input and 3D position supervision, however, outperforms all existing fully supervised image-based methods. Experiments on the challenging KITTI dataset show that our method outperforms the state-of-the-art stereo-based method by around 30% AP on both 3D detection and 3D localization tasks. Code will be made publicly available.

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Metadata

Authors
Li, P., Chen, X., & Shen, S.
Venue
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2019-06
Pages
7636-7644
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2019.00783
OpenAlex
W2962807143
Cited by
593
References
32
FWCI
28.25
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Stereo R-CNN Based 3D Object Detection for Autonomous Driving. https://app.cochat.ai/papers/10.1109/cvpr.2019.00783
The paper
Li, P., Chen, X., & Shen, S. (2019). Stereo R-CNN Based 3D Object Detection for Autonomous Driving. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00783

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