Feifan Ma — accepted AI-conference papers

2023

EB-LG Module for 3D Point Cloud Classification and Segmentation

Jintao Chen, Yan Zhang, Feifan Ma, Zhuangbin Tan

RA-L 2023

Thanks to the development of deep learning technology and computer science, 3D point cloud analysis is becoming a research hotspot. Based on convolution operator, local feature learning is fundamental but far from perfect regarding point cloud analysis task. Actually, when people see a wing-like par

Cited by 15 Source ↗Scholar ↗

2023

Self-Supervised Boundary Point Prediction Task for Point Cloud Domain Adaptation

Jintao Chen, Yan Zhang, Kun Huang, Feifan Ma, Zhuangbin Tan, Zheyu Xu

RA-L 2023

Unsupervised domain adaptation (UDA) could significantly improve the cross-domain performance of current supervised 3D deep learning methods and have a widespread application prospect. However, the domain gap between source domain and target domain renders the UDA problem highly challenging. In this

Cited by 10 Source ↗Scholar ↗