no code implementations • 30 Mar 2023 • Chuer Yu, Xuhong Zhang, Yuxuan Duan, Senbo Yan, Zonghui Wang, Yang Xiang, Shouling Ji, Wenzhi Chen
We then visualize the identity loss between the test and the reference image from the image differences of the aligned pairs, and design a custom metric to quantify the identity loss.
1 code implementation • ICLR 2022 • Liang Peng, Senbo Yan, Boxi Wu, Zheng Yang, Xiaofei He, Deng Cai
This network is learned by minimizing our newly-proposed 3D alignment loss between the 3D box estimates and the corresponding RoI LiDAR points.
no code implementations • 29 Sep 2021 • Liang Peng, Senbo Yan, Chenxi Huang, Xiaofei He, Deng Cai
This characteristic indicates that monocular 3D detection is inherently different from other typical detection tasks that have the same dimensional input and output.
1 code implementation • 19 Apr 2021 • Liang Peng, Fei Liu, Zhengxu Yu, Senbo Yan, Dan Deng, Zheng Yang, Haifeng Liu, Deng Cai
We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels.
no code implementations • 13 Apr 2021 • Liang Peng, Fei Liu, Senbo Yan, Xiaofei He, Deng Cai
Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection.