4D point cloud semantic segmentation - Shi, Hanyu (Nanyang Technological University, 2023)
3D point cloud semantic segmentation is a fundamental scene understanding task. Typical 3D point cloud semantic segmentation approaches analyze the 3D information of LiDAR point clouds and predict the classes of every point in the point cloud scenes. However, existing 3D-based approaches still cannot fulfil the requirements of real-world applications in terms of accuracy. Considering that environments in the wild are dynamic, temporal information is an important clue for identifying dynamic objects and can potentially enhance 3D segmentation models. Therefore, 4D point cloud semantic segmentation is proposed to fully use the temporal and spatial information from 4D point clouds to enhance the performance of existing 3D works. On the other hand, training an effective 4D or 3D segmentation model requires a huge amount of data while manual annotations of point clouds are expensive. Weakly supervised segmentation approaches on 4D point clouds are able to train the segmentation model with minimum annotation requirements. In this thesis, we study fully supervised 4D point cloud semantic segmentation and further weakly supervised segmentation methods on 4D point clouds.