Mobile QR Code QR CODE

2025

Reject Ratio

81.5%

References

1 
J. Sun , L. Chen , Y. Xie , S. Zhang , Q. Jiang , X. Zhou , H. Bao , Disp R-CNN: Stereo 3D object detection via shape prior guided instance disparity estimation, Proc. of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10548-10557, 2020DOI
2 
Y. Wang , J. Yin , W. Li , P. Frossard , R. Yang , J. Shen , SSDA3D: Semi-supervised domain adaptation for 3D object detection from point cloud, Proc. of the AAAI Conference on Artificial Intelligence, Vol. 37, No. 3, pp. 2707-2715, 2023DOI
3 
S. Shi , X. Wang , H. Li , PointRCNN: 3D object proposal generation and detection from point cloud, Proc. of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-779, 2019DOI
4 
C. R. Qi , W. Liu , C. Wu , H. Su , L. J. Guibas , Frustum PointNets for 3D object detection from RGB-D data, Proc. of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 918-927, 2018DOI
5 
C. R. Qi , H. Su , K. Mo , L. J. Guibas , PointNet: Deep learning on point sets for 3D classification and segmentation, Proc. of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652-660, 2017DOI
6 
H. Lin , Y. Zhang , S. Niu , S. Cui , Z. Li , MonoTTA: Fully test-time adaptation for monocular 3D object detection, Proc. of Computer Vision–ECCV 2024, pp. 96-114, 2025DOI
7 
Z. Chen , S. Xu , M. Ye , Z. Qian , X. Zou , D.-Y. Yeung , Q. Chen , Learning high-resolution vector representation from multi-camera images for 3D object detection, Proc. of Computer Vision–ECCV 2024, pp. 385-403, 2025DOI
8 
H. Xie , W. Zheng , Y. Chen , H. Shin , Camera- and LiDAR-based point-painted voxel region-based convolutional neural network for robust 3D object detection, Journal of Electronic Imaging, Vol. 31, No. 5, Art. no. 053025, 2022DOI
9 
H. Sun , Y. Pang , J. Cao , J. Xie , X. Li , Transformer-based stereo-aware 3D object detection from binocular images, IEEE Transactions on Intelligent Transportation Systems, 2024DOI
10 
Y. Ranasinghe , D. Hegde , V. M. Patel , MonoDiff: Monocular 3D object detection and pose estimation with diffusion models, Proc. of 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10659-10670, 2024DOI
11 
P. Li , X. Chen , S. Shen , Stereo R-CNN based 3D object detection for autonomous driving, Proc. of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7644-7652, 2019DOI
12 
Y. Wang , W.-L. Chao , D. Garg , B. Hariharan , M. Campbell , K. Q. Weinberger , Pseudo-LiDAR from visual depth estimation: Bridging the gap in 3D object detection for autonomous driving, Proc. of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8445-8453, 2019DOI
13 
D. J. Jobson , Z. Rahman , G. A. Woodell , A multiscale Retinex for bridging the gap between color images and the human observation of scenes, IEEE Transactions on Image Processing, Vol. 6, No. 7, pp. 965-976, 1997DOI
14 
F. Durand , J. Dorsey , Fast bilateral filtering for the display of high-dynamic-range images, Proc. of the 29th Annual Conference on Computer Graphics and Interactive Techniques, pp. 257-266, 2002DOI
15 
E. Reinhard , M. Stark , P. Shirley , J. Ferwerda , Photographic tone reproduction for digital images, Seminal Graphics Papers: Pushing the Boundaries, Vol. 2, pp. 661-670, 2023DOI
16 
G. Larson , H. Rushmeier , C. Piatko , A visibility matching tone reproduction operator for high dynamic range scenes, IEEE Transactions on Visualization and Computer Graphics, Vol. 3, No. 4, pp. 291-306, 1997DOI
17 
J. Duan , G. Qiu , Fast tone mapping for high dynamic range images, Proc. of the 17th International Conference on Pattern Recognition, Vol. 2, pp. 847-850, 2004DOI
18 
C. R. Qi , L. Yi , H. Su , L. J. Guibas , PointNet++: Deep hierarchical feature learning on point sets in a metric space, Advances in Neural Information Processing Systems, Vol. 30, 2017Google Search
19 
A. Geiger , P. Lenz , R. Urtasun , Are we ready for autonomous driving? The KITTI vision benchmark suite, Proc. of 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354-3361, 2012DOI
20 
G. Yang , X. Song , C. Huang , Z. Deng , J. Shi , B. Zhou , DrivingStereo: A large-scale dataset for stereo matching in autonomous driving scenarios, Proc. of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 899-908, 2019DOI
21 
X. Guo , S. Shi , X. Wang , H. Li , LIGA-Stereo: Learning LiDAR geometry-aware representations for stereo-based 3D detector, Proc. of 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3153-3163, 2021DOI
22 
Y. Chen , S. Huang , S. Liu , B. Yu , J. Jia , DSGN++: Exploiting visual-spatial relation for stereo-based 3D detectors, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, No. 4, pp. 4416-4429, 2023DOI
23 
Y. Zhang , J. Zhang , X. Guo , Kindling the darkness: A practical low-light image enhancer, Proc. of the 27th ACM International Conference on Multimedia, pp. 1632-1640, 2019DOI
24 
M. Lamba , M. V. A. Kumar , K. Mitra , Real-time restoration of dark stereo images, Proc. of 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4903-4913, 2023DOI