no code implementations • 19 Mar 2024 • Ying Chen, Yong liu, Kai Wu, Qiang Nie, Shang Xu, Huifang Ma, Bing Wang, Chengjie Wang
Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands.
no code implementations • 10 May 2023 • Zhuofei Huang, Jianlin Liu, Shang Xu, Ying Chen, Yong liu
Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces.
no code implementations • 31 Aug 2022 • Jianlin Liu, Zhuofei Huang, Dihe Huang, Shang Xu, Ying Chen, Yong liu
3D object detection from monocular image(s) is a challenging and long-standing problem of computer vision.
1 code implementation • CVPR 2023 • Dihe Huang, Ying Chen, Shang Xu, Yong liu, Wenlong Wu, Yikang Ding, Chengjie Wang, Fan Tang
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance.
1 code implementation • 25 May 2022 • Yimin Ou, Rui Yang, Lufan Ma, Yong liu, Jiangpeng Yan, Shang Xu, Chengjie Wang, Xiu Li
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e. g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple duplicated predictions.
1 code implementation • 18 Feb 2022 • Ying Chen, Dihe Huang, Shang Xu, Jianlin Liu, Yong liu
Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important.