Weakly supervised point cloud segmentation, ie semantically segmenting a point cloud with
only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden …

In this study, we introduce PlantSegNet, a novel neural network model for instance
segmentation of nearby objects with similar geometric structures. Our work addresses the …

Existing state-of-the-art 3D point clouds understanding methods merely perform well in a
fully supervised manner. To the best of our knowledge, there exists no unified framework …

Weakly-supervised point cloud segmentation with extremely limited labels is highly
desirable to alleviate the expensive costs of collecting densely annotated 3D points. This …

Collecting and labeling the registered 3D point cloud is costly. As a result, 3D resources for
training are typically limited in quantity compared to the 2D images counterpart. In this work …

Existing fully supervised deep learning methods usually require a large number of training
samples with abundant annotations for the model training, which is extremely expensive and …