Authors:
Thanasis Zoumpekas
1
;
2
;
Guillem Molina
1
;
Anna Puig
1
and
Maria Salamó
1
Affiliations:
1
Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
;
2
Unit Industrial Software Applications, RISC Software GmbH, Softwarepark 35, Hagenberg, Austria
Keyword(s):
Segmentation, Point Clouds, Analysis, Dashboard, Data Visualization, Deep Learning.
Abstract:
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.