4 citations · 6 across the 7 of their papers we have counts for
7 papers
Mamba Learns in Context: Structure-Aware Domain Generalization for Multi-Task Point Cloud Understanding
Jincen Jiang, Qianyu Zhou, Yuhang Li +5
While recent Transformer and Mamba architectures have advanced point cloud representation learning, they are typically developed for single-task or single-domain settings. Directly…
PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding
Jincen Jiang, Qianyu Zhou, Yuhang Li +6
In this paper, we present PCoTTA, an innovative, pioneering framework for Continual Test-Time Adaptation (CoTTA) in multi-task point cloud understanding, enhancing the model's tran…
DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding
Jincen Jiang, Qianyu Zhou, Yuhang Li +5
Recent point cloud understanding research suffers from performance drops on unseen data, due to the distribution shifts across different domains. While recent studies use Domain Ge…
DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning
Jincen Jiang, Lizhi Zhao, Xuequan Lu +3
Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks. These works tend to sample and group points to create sm…
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…
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…
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…