no code implementations • 3 Jul 2022 • Wu Zheng, Li Jiang, Fanbin Lu, Yangyang Ye, Chi-Wing Fu
To boost a detector for single-frame 3D object detection, we present a new approach to train it to simulate features and responses following a detector trained on multi-frame point clouds.
no code implementations • CVPR 2022 • Wu Zheng, Mingxuan Hong, Li Jiang, Chi-Wing Fu
This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector.
1 code implementation • CVPR 2021 • Wu Zheng, Weiliang Tang, Li Jiang, Chi-Wing Fu
Lastly, to better exploit hard targets, we design an ODIoU loss to supervise the student with constraints on the predicted box centers and orientations.
Ranked #1 on Birds Eye View Object Detection on KITTI Cars Easy
1 code implementation • 5 Dec 2020 • Wu Zheng, Weiliang Tang, Sijin Chen, Li Jiang, Chi-Wing Fu
Existing single-stage detectors for locating objects in point clouds often treat object localization and category classification as separate tasks, so the localization accuracy and classification confidence may not well align.
Ranked #3 on Birds Eye View Object Detection on KITTI Cars Easy
no code implementations • 7 May 2018 • Wu Zheng, Lin Li, Zhao-Xiang Zhang, Yan Huang, Liang Wang
We introduce the Recurrent Relational Network to learn the spatial features in a single skeleton, followed by a multi-layer LSTM to learn the temporal features in the skeleton sequences.
Ranked #95 on Skeleton Based Action Recognition on NTU RGB+D