Partial Point Cloud

Partial point cloud research focuses on addressing the incompleteness of 3D point cloud data, often caused by occlusion or sensor limitations, aiming to reconstruct the missing parts of objects. Current research employs various deep learning architectures, including generative adversarial networks (GANs), transformers, and graph neural networks, often incorporating self-supervised learning techniques to overcome the lack of complete ground truth data for training. These advancements are crucial for improving the robustness and accuracy of 3D perception in applications such as autonomous driving, robotics, and 3D modeling, where complete point cloud data is often unavailable. The development of efficient and accurate partial point cloud completion methods is driving significant progress in these fields.