Title: Autonomous void detection and characterisation in point clouds and triangular meshes

Authors: Benjamin Bird; Thomas Wright; Simon Watson; Barry Lennox

Addresses: Department of Electrical and Electronic Engineering, University of Manchester, Manchester, UK ' Department of Electrical and Electronic Engineering, University of Manchester, Manchester, UK ' Department of Electrical and Electronic Engineering, University of Manchester, Manchester, UK ' Department of Electrical and Electronic Engineering, University of Manchester, Manchester, UK

Abstract: In this paper we propose and demonstrate a novel void characterisation algorithm which is able to distinguish between internal and external voids that are present in point clouds of both manifold and non-manifold objects and 3D scenes. We demonstrate the capabilities of our algorithm using several point clouds representing both scenes and objects. Our algorithm is shown in both a descriptive overview format as well as pseudocode. We also compare a variety of different void detection algorithms and then present a novel refinement to the best performing of these algorithms. Our refinement allows for voids in point clouds to be detected more efficiently, with fewer false positives and with over an order of magnitude improvement in terms of run time. We show our run time performance and compare it to results obtained using alternative algorithms, when tested using popular single board computers. This comparison is important as our work is intended for online robotics applications, where hardware is typically of low computational power. The target application for this work is 3D scene reconstruction to aid in the decommissioning of nuclear facilities.

Keywords: point cloud; void detection; meshing; reconstruction; computer vision.

DOI: 10.1504/IJCVR.2019.101538

International Journal of Computational Vision and Robotics, 2019 Vol.9 No.4, pp.368 - 386

Received: 03 May 2018
Accepted: 11 Jul 2018
Published online: 12 Aug 2019 *

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