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arXiv · 2209.08221

Neural Implicit Surface Reconstruction using Imaging Sonar

Mohamad Qadri·Michael Kaess·Ioannis Gkioulekas
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Abstract

We present a technique for dense 3D reconstruction of objects using an imaging sonar, also known as forward-looking sonar (FLS). Compared to previous methods that model the scene geometry as point clouds or volumetric grids, we represent the geometry as a neural implicit function. Additionally, given such a representation, we use a differentiable volumetric renderer that models the propagation of acoustic waves to synthesize imaging sonar measurements. We perform experiments on real and synthetic datasets and show that our algorithm reconstructs high-fidelity surface geometry from multi-view FLS images at much higher quality than was possible with previous techniques and without suffering from their associated memory overhead.

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Mohamad Qadri, Michael Kaess, Ioannis Gkioulekas. 2022-09-17. Neural Implicit Surface Reconstruction using Imaging Sonar. https://arxiv.org/abs/2209.08221

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