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Research Article | Open Access

Hybrid mesh-neural representation for 3D transparent object reconstruction

College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, China
College of Computer Science, ETH Zürich, Zürich 8092, Switzerland
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Graphical Abstract

Abstract

In this study, we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting conditions. It combines the advantages of an explicit mesh and multi-layer perceptron (MLP) network as a hybrid representation to simplify the capture settings used in recent studies. After obtaining an initial shape through multi-view silhouettes, we introduced surface-based local MLPs to encode the vertex displacement field (VDF) for reconstructing surface details. The design of local MLPs allowed representation of the VDF in a piecewise manner using two-layer MLP networks to support the optimization algorithm. Defining local MLPs on the surface instead of on the volume also reduced the search space. Such a hybrid representation enabled us to relax the ray–pixel correspondences that represent the light path constraint to our designed ray–cell correspondences, which significantly simplified the implementation of a single-image-based environment-matting algorithm. We evaluated our representation and reconstruction algorithm on several transparent objects based on ground truth models. The experimental results show that our method produces high-quality reconstructions that are superior to those of state-of-the-art methods using a simplified data-acquisition setup.

Keywords

transparent object3D reconstructionenvironment mattingneural rendering

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Computational Visual Media
Volume 11 Issue 1,
February 2025
Pages 123-140
DOI: 10.26599/CVM.2025.9450328
Cite this article:
Xu J, Zhu Z, Bao H, et al. Hybrid mesh-neural representation for 3D transparent object reconstruction. Computational Visual Media, 2025, 11(1): 123-140. https://doi.org/10.26599/CVM.2025.9450328

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Received: 22 August 2022
Accepted: 18 December 2022
Published: 28 February 2025
© The Author(s) 2025.

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