Gu Wang

Orcid: 0000-0002-0759-0782

Affiliations:
  • Tsinghua University, Beijing, China


According to our database1, Gu Wang authored at least 13 papers between 2016 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation.
IEEE Trans. Pattern Anal. Mach. Intell., March, 2024

BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects.
CoRR, 2024

2023
HACD: Hand-Aware Conditional Diffusion for Monocular Hand-Held Object Reconstruction.
CoRR, 2023

MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision.
CoRR, 2023

BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
CATRE: Iterative Point Clouds Alignment for Category-Level Object Pose Refinement.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
DeepIM: Deep Iterative Matching for 6D Pose Estimation.
Int. J. Comput. Vis., 2020

Self6D: Self-supervised Monocular 6D Object Pose Estimation.
Proceedings of the Computer Vision - ECCV 2020, 2020

PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2016
Action Recognition with Joint Attention on Multi-Level Deep Features.
CoRR, 2016


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