Pixel-Based Vertex Clustering for Spectral Data Enrichment of Planar Point Clouds

Application of both hyperspectral imaging and 3D point clouds have been utilized in various fields that investigates a surface’s geometrical properties and its material characteristics. The two modalities are acquired using different sensors and must subsequently be fused in order to be analyzed in a common dataspace. This fusion is a complex and time-consuming process, and has only recently been investigated properly with remote sensing. We propose an approachable and fast workflow that highlights the most important aspect of each method for applications with proximity imaging, meant to be used for quick investigations of an objects geometric surface and its material characteristics. Data is collected using commercial instruments, and simplifies the data while retaining and highlighting the most important geometrical and spectral features.