Semantic segmentation of point clouds with the 3D medial axis transform

Master thesis (2020)

Authors

G. Ceccarelli Architecture and the Built Environment

Contributors

R.Y. Peters Urban Data Science - Architecture and the Built Environment (supervisor 1)
W. Gao Urban Data Science - Architecture and the Built Environment (supervisor 1)
Faculty
Architecture and the Built Environment
Copyright: © 2020 G. Ceccarelli
More Info
expand_more

Published Date

03-07-2020

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Architecture and the Built Environment

Abstract

A point cloud is a representation of shapes, organized in a 3D irregular structure. Point clouds are increasingly used in different applications, ranging from architectural preservation to computer vision. The 3D medial axis transform is a topology preserving, skeleton representation of shapes. It can be used to decompose an object in meaningful parts and to describe local and long range information of points in a point cloud.

In the past years, many deep learning methods for point clouds emerged. These are used for different applications, such as shape classification, object detection or semantic segmentation. In particular, the latter aim to classify each point in the input point cloud in subsets, based on their semantics.

This research investigates the integration of the 3D MAT in two deep learning methods for point clouds' semantic segmentation, PointNet++ and Superpoint Graph. In particular, the 3D MAT was used in PointNet++ as a point feature, to give context to local points. Then, it was used in Superpoint Graph as a geometric descriptor to partition a point cloud and as a edge feature in the SPG.

The major findings of this research outline that the 3D MAT can be successfully used in PointNet++ as a point feature, improving the overall accuracy and loss values of the algorithm. Particularly two MAT derived properties used in this research output positive results, radii and separation angles. These can be combined with point coordinates and RGB information to bring additional knowledge on the geometry of the shape, representing its curvature and thickness. Furthermore, they can be integrated in a simple and effective way, without increasing computational or time effort in the algorithm.

The analysis carried out in Superpoint Graph depicts that the 3D MAT does not improve the initial geometric partition. In fact, adding geometric descriptors to the algorithm increases the difficulty in dividing the point cloud into simple shapes, creating artifacts. Furthermore, adding MAT information on superedges does not give added value to the SPG graph. The reason is that the SPG graph and the structured MAT are different than each other, in practice, as nodes represent diverse parts in the point cloud.