Enhanced GPIS learning based on local and global focus areas
Paper in proceeding, 2023

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 including data from robot visual and tactile sensors. Results show that our approach leads to high accuracy compared to the baselines.

Surface Reconstruction

Robotics

Gaussian Processes

Gaussian Process Implicit Surfaces

Author

Zuka Murvanidze

University College London (UCL)

Marc Peter Deisenroth

University College London (UCL)

Yasemin Bekiroglu

Chalmers, Electrical Engineering, Systems and control

Proceedings - IEEE International Conference on Robotics and Automation

10504729 (ISSN)

IEEE International Conference on Robotics and Automation (ICRA)
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Subject Categories

Robotics

Computer Science

Computer Vision and Robotics (Autonomous Systems)

More information

Created

12/20/2023