Enhanced GPIS Learning Based on Local and Global Focus Areas

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Enhanced GPIS Learning Based on Local and Global Focus Areas

Murvanidze, Z; Deisenroth, MP; Bekiroglu, Y; (2022) Enhanced GPIS Learning Based on Local and Global Focus Areas. IEEE Robotics and Automation Letters 10.1109/LRA.2022.3197905. (In press). Green open access

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Abstract

Implicit surface learning is one of the most widely used methods for 3D surface reconstruction from raw point cloud data. Current approaches employ deep neural networks or Gaussian process models with the trade-offs across computational performance, object fidelity, and generalization capabilities. We propose a novel method based on Gaussian process regression to build implicit surfaces for 3D surface reconstruction (GPIS), which leads to better accuracy in comparison to the standard GPIS formulation. Our approach encodes local and global shape information from the data to maintain the correct topology of the underlying shape. The proposed pipeline works on dense, sparse, and noisy raw point clouds and can be parallelized to improve computational efficiency. We evaluate our approach on synthetic and real point cloud datasets obtained from laser scans, synthetic CAD objects and robot visual and tactical sensors. Results show that our approach leads to high accuracy compared to baselines.

Type:	Article
Title:	Enhanced GPIS Learning Based on Local and Global Focus Areas
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1109/LRA.2022.3197905
Publisher version:	https://doi.org/10.1109/LRA.2022.3197905
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Perception for Grasping and Manipulation, Visual Learning
UCL classification:	UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL
URI:	https://discovery.ucl.ac.uk/id/eprint/10155177