most citedSE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation

10 citations · 14 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV202310 cited

SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation

Haobo Jiang, Mathieu Salzmann, Zheng Dang +2

In this paper, we introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D…

cs.CV20232 cited

Robust Outlier Rejection for 3D Registration with Variational Bayes

Haobo Jiang, Zheng Dang, Zhen Wei +3

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The co…

cs.CV2023

Recurrent Structure Attention Guidance for Depth Super-Resolution

Jiayi Yuan, Haobo Jiang, Xiang Li +3

Image guidance is an effective strategy for depth super-resolution. Generally, most existing methods employ hand-crafted operators to decompose the high-frequency (HF) and low-freq…

cs.CV20231 cited

Structure Flow-Guided Network for Real Depth Super-Resolution

Jiayi Yuan, Haobo Jiang, Xiang Li +3

Real depth super-resolution (DSR), unlike synthetic settings, is a challenging task due to the structural distortion and the edge noise caused by the natural degradation in real-wo…

cs.CV2022

Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking

Haobo Jiang, Kaihao Lan, Le Hui +3

Learning robust feature matching between the template and search area is crucial for 3D Siamese tracking. The core of Siamese feature matching is how to assign high feature similar…

cs.LG20221 cited

Generative Subgraph Contrast for Self-Supervised Graph Representation Learning

Yuehui Han, Le Hui, Haobo Jiang +2

Contrastive learning has shown great promise in the field of graph representation learning. By manually constructing positive/negative samples, most graph contrastive learning meth…