98 citations · 168 across the 31 of their papers we have counts for
9 papers · 1 filter
Learning Inter-Superpoint Affinity for Weakly Supervised 3D Instance Segmentation
Linghua Tang, Le Hui, Jin Xie
Due to the few annotated labels of 3D point clouds, how to learn discriminative features of point clouds to segment object instances is a challenging problem. In this paper, we pro…
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…
Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching
Yikai Bian, Le Hui, Jianjun Qian +1
Unsupervised domain adaptation for point cloud semantic segmentation has attracted great attention due to its effectiveness in learning with unlabeled data. Most of existing method…
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…
3D Siamese Transformer Network for Single Object Tracking on Point Clouds
Le Hui, Lingpeng Wang, Linghua Tang +3
Siamese network based trackers formulate 3D single object tracking as cross-correlation learning between point features of a template and a search area. Due to the large appearance…
RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation
Mu He, Le Hui, Yikai Bian +3
Existing self-supervised monocular depth estimation methods can get rid of expensive annotations and achieve promising results. However, these methods suffer from severe performanc…
Unsupervised Visible-light Images Guided Cross-Spectrum Depth Estimation from Dual-Modality Cameras
Yubin Guo, Haobo Jiang, Xinlei Qi +3
Cross-spectrum depth estimation aims to provide a depth map in all illumination conditions with a pair of dual-spectrum images. It is valuable for autonomous vehicle applications w…
Action Candidate Driven Clipped Double Q-learning for Discrete and Continuous Action Tasks
Haobo Jiang, Jin Xie, Jian Yang
Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped Double Q-learning, as an effective variant of Double Q-learning,…
Reliable Inlier Evaluation for Unsupervised Point Cloud Registration
Yaqi Shen, Le Hui, Haobo Jiang +2
Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier…