CLOSED: A Dashboard for 3D Point Cloud Segmentation Analysis using Deep Learning

With the growing interest in 3D point cloud data, which is a set of data points in space used to describe a 3D object, and the inherent need to analyze it using deep neural networks, the visualization of data processes is critical for extracting meaningful insights. There is a gap in the literature for a full-suite visualization tool to analyse 3D deep learning segmentation models on point cloud data. This paper proposes such a tool to cover this gap, entitled point CLOud SEgmentation Dashboard (CLOSED). Specifically, we concentrate our efforts on 3D point cloud part segmentation. where the entire shape and the parts of a 3D object are significant. Our approach manages to (i) exhibit the learning evolution of neural networks, (ii) compare and evaluate different neural networks, (iii) highlight key-points of the segmentation process. We illustrate our proposal by analysing five neural networks utilizing the ShapeNet-part dataset.