Gangwei Xu

Orcid: 0009-0007-0435-4534

According to our database1, Gangwei Xu authored at least 15 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Accurate and Efficient Stereo Matching via Attention Concatenation Volume.
IEEE Trans. Pattern Anal. Mach. Intell., April, 2024

Coatrsnet: Fully Exploiting Convolution and Attention for Stereo Matching by Region Separation.
Int. J. Comput. Vis., January, 2024

IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching.
CoRR, 2024

HDRFlow: Real-Time HDR Video Reconstruction with Large Motions.
CoRR, 2024

Hybrid Cost Volume for Memory-Efficient Optical Flow.
Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

Masked Snake Attention for Fundus Image Restoration with Vessel Preservation.
Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

HDRFlow: Real-Time HDR Video Reconstruction with Large Motions.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

MC-Stereo: Multi-Peak Lookup and Cascade Search Range for Stereo Matching.
Proceedings of the International Conference on 3D Vision, 2024

2023
FlowDA: Unsupervised Domain Adaptive Framework for Optical Flow Estimation.
CoRR, 2023

Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume.
CoRR, 2023

CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction.
CoRR, 2023

Iterative Geometry Encoding Volume for Stereo Matching.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
ACVNet: Attention Concatenation Volume for Accurate and Efficient Stereo Matching.
CoRR, 2022

Attention Concatenation Volume for Accurate and Efficient Stereo Matching.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022


  Loading...