Faithful Euclidean Distance Field from Log-Gaussian Process Implicit Surfaces
Lan Wu, Ki Myung Brian Lee, Liyang Liu, Teresa Vidal-Calleja

TL;DR
This paper introduces Log-Gaussian Process Implicit Surface (Log-GPIS), a probabilistic method for accurate Euclidean distance field and surface reconstruction that enables real-time applications without post-processing.
Contribution
The authors develop a novel approach that solves the Eikonal equation via a logarithmic transformation of GPIS, directly estimating distance fields and surface normals without sampling or post-processing.
Findings
Log-GPIS accurately recovers Euclidean distance fields.
It achieves comparable surface reconstruction results to state-of-the-art methods.
The method supports online computation suitable for real-time applications.
Abstract
In this letter, we introduce the Log-Gaussian Process Implicit Surface (Log-GPIS), a novel continuous and probabilistic mapping representation suitable for surface reconstruction and local navigation. Our key contribution is the realisation that the regularised Eikonal equation can be simply solved by applying the logarithmic transformation to a GPIS formulation to recover the accurate Euclidean distance field (EDF) and, at the same time, the implicit surface. To derive the proposed representation, Varadhan's formula is exploited to approximate the non-linear Eikonal partial differential equation (PDE) of the EDF by the logarithm of a linear PDE. We show that members of the Matern covariance family directly satisfy this linear PDE. The proposed approach does not require post-processing steps to recover the EDF. Moreover, unlike sampling-based methods, Log-GPIS does not use sample points…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobotics and Sensor-Based Localization · Gaussian Processes and Bayesian Inference · 3D Shape Modeling and Analysis
