Volltext-Downloads (blau) und Frontdoor-Views (grau)

Effective Neighborhood Feature Exploitation in Graph CNNs for Point Cloud Object-Part Segmentation

  • Part segmentation is the task of semantic segmentation applied on objects and carries a wide range of applications from robotic manipulation to medical imaging. This work deals with the problem of part segmentation on raw, unordered point clouds of 3D objects. While pioneering works on deep learning for point clouds typically ignore taking advantage of local geometric structure around individual points, the subsequent methods proposed to extract features by exploiting local geometry have not yielded significant improvements either. In order to investigate further, a graph convolutional network (GCN) is used in this work in an attempt to increase the effectiveness of such neighborhood feature exploitation approaches. Most of the previous works also focus only on segmenting complete point cloud data. Considering the impracticality of such approaches, taking into consideration the real world scenarios where complete point clouds are scarcely available, this work proposes approaches to deal with partial point cloud segmentation.     In the attempt to better capture neighborhood features, this work proposes a novel method to learn regional part descriptors which guide and refine the segmentation predictions. The proposed approach helps the network achieve state-of-the-art performance of 86.4% mIoU on the ShapeNetPart dataset for methods which do not use any preprocessing techniques or voting strategies. In order to better deal with partial point clouds, this work also proposes new strategies to train and test on partial data. While achieving significant improvements compared to the baseline performance, the problem of partial point cloud segmentation is also viewed through an alternate lens of semantic shape completion.     Semantic shape completion networks not only help deal with partial point cloud segmentation but also enrich the information captured by the system by predicting complete point clouds with corresponding semantic labels for each point. To this end, a new network architecture for semantic shape completion is also proposed based on point completion network (PCN) which takes advantage of a graph convolution based hierarchical decoder for completion as well as segmentation. In addition to predicting complete point clouds, results indicate that the network is capable of reaching within a margin of 5% to the mIoU performance of dedicated segmentation networks for partial point cloud segmentation.

Download full text files

Export metadata

Additional Services

Search Google Scholar Check availability


Show usage statistics
Document Type:Report
Author:Pranav Megarajan
Number of pages:xvi, 120
Supervisor:Ulrich Hillenbrand, Paul G. Plöger, Rudolph Triebel
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Contributing Corporation:Bonn-Aachen International Center for Information Technology (b-it); Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Date of first publication:2022/03/11
Series (Volume):Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences. Department of Computer Science (01-2022)
Keyword:3D Segmentation; Graph Convolutional Neural Networks; Object Segmentation; Part Segmentation; Point Cloud Segmentation; Semantic Segmentation
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Series:Technical Report / University of Applied Sciences Bonn-Rhein-Sieg. Department of Computer Science
Entry in this database:2022/03/11
Licence (Multiple languages):License LogoIn Copyright - Educational Use Permitted (Urheberrechtsschutz - Nutzung zu Bildungszwecken erlaubt)