Semantic labeling and instance segmentation of 3D point clouds using patch context analysis and multiscale processing

Hu, Shi-Min ORCID: https://orcid.org/0000-0001-7507-6542, Cai, Jun-Xiong and Lai, Yu-kun ORCID: https://orcid.org/0000-0002-2094-5680 2020. Semantic labeling and instance segmentation of 3D point clouds using patch context analysis and multiscale processing. IEEE Transactions on Visualization and Computer Graphics 26 (7) , pp. 2485-2498. 10.1109/TVCG.2018.2889944

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Abstract

We present a novel algorithm for semantic segmentation and labeling of 3D point clouds of indoor scenes, where objects in point clouds can have significant variations and complex configurations. Effective segmentation methods decomposing point clouds into semantically meaningful pieces are highly desirable for object recognition, scene understanding, scene modeling, etc. However, existing segmentation methods based on low-level geometry tend to either under-segment or over-segment point clouds. Our method takes a fundamentally different approach, where semantic segmentation is achieved along with labeling. To cope with substantial shape variation for objects in the same category, we first segment point clouds into surface patches and use unsupervised clustering to group patches in the training set into clusters, providing an intermediate representation for effectively learning patch relationships. During testing, we propose a novel patch segmentation and classification framework with multiscale processing, where the local segmentation level is automatically determined by exploiting the learned cluster based contextual information. Our method thus produces robust patch segmentation and semantic labeling results, avoiding parameter sensitivity. We further learn object-cluster relationships from the training set, and produce semantically meaningful object level segmentation.Our method outperforms state-of-the-art methods on several representative point cloud datasets, including S3DIS, SceneNN, Cornell RGB-D and ETH.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	1077-2626
Date of First Compliant Deposit:	25 December 2018
Date of Acceptance:	26 November 2018
Last Modified:	07 Nov 2023 21:40
URI:	https://orca.cardiff.ac.uk/id/eprint/117901

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