no code implementations • 28 Sep 2023 • Xiaogang Jia, Songlei Jian, Yusong Tan, Yonggang Che, Wei Chen, Zhengfa Liang
With a simple yet efficient gating mechanism, our proposed method achieves fast and accurate depth completion without the need for additional branches or post-processing steps.
no code implementations • 6 Mar 2023 • Yulin He, Wei Chen, Ke Liang, Yusong Tan, Zhengfa Liang, Yulan Guo
Our proposed method, Pseudo-label Correction and Learning (PCL), is extensively evaluated on the MS COCO and PASCAL VOC benchmarks.
no code implementations • 3 Feb 2021 • Xiaogang Jia, Wei Chen, Zhengfa Liang, Mingfei Wu, Yusong Tan, Libo Huang
This is because different cost volumes play a crucial role in balancing speed and accuracy.
1 code implementation • 16 Sep 2020 • Longguang Wang, Yulan Guo, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An
Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks.
1 code implementation • CVPR 2019 • Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo
Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint.
Ranked #1 on Image Super-Resolution on KITTI 2012 - 4x upscaling
2 code implementations • CVPR 2018 • Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, Jianfeng Zhang
The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.