A data fusion pipeline for registering point clouds with novel characteristics: enabling the computer to recognise a pattern without training dataset - Zhang, Zhongyi Michael (2025)
Data fusion is the technical process which can provide comprehensive information about an object by combining multiple datasets that are collected by different sensors. It has been employed for point cloud registration in the context of optical coordinate measurement, an important subject in metrology. Researchers in this field have proposed numerous methods to improve the performance of data fusion, which can be categorised into user-dependent methods, including Gaussian process (GP) and weighted least-squares (WLS) algorithms, and user-independent methods such as machine learning. Recent research has shown the convenience of deploying GP and WLS and the flexibility and autonomous functionality of machine learning solutions. However, the target scenarios have been focused on point clouds in similar sizes and point densities. This trend leaves room for further innovation in point cloud registration.