Jiehong Lin

According to our database1, Jiehong Lin authored at least 13 papers between 2019 and 2023.

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

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2023
SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation.
CoRR, 2023

Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
Geometry-Aware Generation of Adversarial Point Clouds.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning.
CoRR, 2022

Masked Surfel Prediction for Self-Supervised Point Cloud Learning.
CoRR, 2022

Category-Level 6D Object Pose and Size Estimation Using Self-supervised Deep Prior Deformation Networks.
Proceedings of the Computer Vision - ECCV 2022, 2022

DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling.
CoRR, 2020

2019
Deep Multi-View Learning Using Neuron-Wise Correlation-Maximizing Regularizers.
IEEE Trans. Image Process., 2019

Geometry-aware Generation of Adversarial and Cooperative Point Clouds.
CoRR, 2019


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