196 citations · 822 across the 56 of their papers we have counts for
45 papers · 1 filter
Weighted Point Cloud Normal Estimation
Weijia Wang, Xuequan Lu, Di Shao +4
Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures. Also, they usually ignore the contributions of different…
Don't worry about mistakes! Glass Segmentation Network via Mistake Correction
Chengyu Zheng, Peng Li, Xiao-Ping Zhang +2
Recall one time when we were in an unfamiliar mall. We might mistakenly think that there exists or does not exist a piece of glass in front of us. Such mistakes will remind us to w…
Instance-Aware Domain Generalization for Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao +4
Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain…
IterativePFN: True Iterative Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao +3
The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cl…
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
Yuhao Chen, Xin Tan, Borui Zhao +4
Semi-supervised learning (SSL) has attracted enormous attention due to its vast potential of mitigating the dependence on large labeled datasets. The latest methods (e.g., FixMatch…
Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks
Chen Pang, Xuequan Lu, Lei Lyu
For pursuing accurate skeleton-based action recognition, most prior methods use the strategy of combining Graph Convolution Networks (GCNs) with attention-based methods in a serial…
Deepfake Detection via Joint Unsupervised Reconstruction and Supervised Classification
Bosheng Yan, Chang-Tsun Li, Xuequan Lu
Deep learning has enabled realistic face manipulation (i.e., deepfake), which poses significant concerns over the integrity of the media in circulation. Most existing deep learning…
Graph Classification via Discriminative Edge Feature Learning
Yang Yi, Xuequan Lu, Shang Gao +2
Spectral graph convolutional neural networks (GCNNs) have been producing encouraging results in graph classification tasks. However, most spectral GCNNs utilize fixed graphs when a…
SPCNet: Stepwise Point Cloud Completion Network
Fei Hu, Honghua Chen, Xuequan Lu +5
How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate th…
SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation
Haoran Pan, Jun Zhou, Yuanpeng Liu +4
6D pose estimation of rigid objects from RGB-D images is crucial for object grasping and manipulation in robotics. Although RGB channels and the depth (D) channel are often complem…
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Gang Li +1
Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and o…
Masked Autoencoders in 3D Point Cloud Representation Learning
Jincen Jiang, Xuequan Lu, Lizhi Zhao +2
Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstre…
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
Jilan Xu, Junlin Hou, Yuejie Zhang +5
Weakly Supervised Object Localization (WSOL) aims to localize objects with image-level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived from a clas…
Towards Uniform Point Distribution in Feature-preserving Point Cloud Filtering
Shuaijun Chen, Jinxi Wang, Wei Pan +3
As a popular representation of 3D data, point cloud may contain noise and need to be filtered before use. Existing point cloud filtering methods either cannot preserve sharp featur…
Rethinking Point Cloud Filtering: A Non-Local Position Based Approach
Jinxi Wang, Jincen Jiang, Xuequan Lu +1
Existing position based point cloud filtering methods can hardly preserve sharp geometric features. In this paper, we rethink point cloud filtering from a non-learning non-local no…
A Robust Scheme for 3D Point Cloud Copy Detection
Jiaqi Yang, Xuequan Lu, Wenzhi Chen
Most existing 3D geometry copy detection research focused on 3D watermarking, which first embeds ``watermarks'' and then detects the added watermarks. However, this kind of methods…
Anatomical Landmarks Localization for 3D Foot Point Clouds
Sheldon Fung, Xuequan Lu, Mantas Mykolaitis +2
3D anatomical landmarks play an important role in health research. Their automated prediction/localization thus becomes a vital task. In this paper, we introduce a deformation meth…
Deep Point Cloud Normal Estimation via Triplet Learning
Weijia Wang, Xuequan Lu, Dasith de Silva Edirimuni +2
Normal estimation on 3D point clouds is a fundamental problem in 3D vision and graphics. Current methods often show limited accuracy in predicting normals at sharp features (e.g.,…
Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data
Jincen Jiang, Xuequan Lu, Wanli Ouyang +1
Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are supervised and require labels for training. By contrast, unsupervise…
Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
Qianyu Zhou, Chuyun Zhuang, Ran Yi +2
Unsupervised domain adaptation (UDA) for semantic segmentation has been well-studied in recent years. However, most existing works largely neglect the local regional consistency ac…
3D Face Recognition: A Survey
Yaping Jing, Xuequan Lu, Shang Gao
Face recognition is one of the most studied research topics in the community. In recent years, the research on face recognition has shifted to using 3D facial surfaces, as more dis…
PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation
Qiqi Gu, Qianyu Zhou, Minghao Xu +5
Cross-domain object detection and semantic segmentation have witnessed impressive progress recently. Existing approaches mainly consider the domain shift resulting from external en…
Context-Aware Mixup for Domain Adaptive Semantic Segmentation
Qianyu Zhou, Zhengyang Feng, Qiqi Gu +5
Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. Existing UDA-based semantic segmentation approaches always re…
Self-Adversarial Disentangling for Specific Domain Adaptation
Qianyu Zhou, Qiqi Gu, Jiangmiao Pang +2
Domain adaptation aims to bridge the domain shifts between the source and the target domain. These shifts may span different dimensions such as fog, rainfall, etc. However, recent…