Recent development of 3D sensing devices and techniques have made collecting 3D data more affordable and faster than ever. The increasing amount of 3D data can provide useful information for tasks with rich content. For example, 3D data, if semantic labelled, can be used for producing detailed 3D models. With a semantic understanding from 3D data, decision-making systems like automatic navigation and car parking can make better decisions. However, semantic labelling 3D data requires time-consuming manual work.
Deep learning, which is a class of machine learning algorithms, can serve as a tool for 3D data processing. GRID aims to develop a deep learning-based end-to-end solution for the following goals:
Our team of experts aims to investigate and develop a deep learning-based solution for 3D data processing. The solution can handle classification and segmentation tasks. Expected deliverables can be listed as follow: