no code implementations • 8 Jul 2021 • Shuang Deng, Qiulei Dong, Bo Liu, Zhanyi Hu
The proposed network is iteratively updated with its predicted pseudo labels, where a superpoint generation module is introduced for extracting superpoints from 3D point clouds, and a pseudo-label optimization module is explored for automatically assigning pseudo labels to the unlabeled points under the constraint of the extracted superpoints.
1 code implementation • 7 Jul 2021 • Shuang Deng, Bo Liu, Qiulei Dong, Zhanyi Hu
Many recent works show that a spatial manipulation module could boost the performances of deep neural networks (DNNs) for 3D point cloud analysis.
no code implementations • 7 Jul 2021 • Shuang Deng, Qiulei Dong
Addressing this problem, we propose a global attention network for point cloud semantic segmentation, named as GA-Net, consisting of a point-independent global attention module and a point-dependent global attention module for obtaining contextual information of 3D point clouds in this paper.
no code implementations • 1 Jul 2021 • Bo Liu, Shuang Deng, Qiulei Dong, Zhanyi Hu
In this work, a language-level Semantics Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of point feature distribution as well as the pseudo-feature generation, and a feature-geometry-based mixup approach is further proposed to facilitate the distribution learning.