Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes

• URL: http://arxiv.org/abs/2004.10904v2
• Date: Thu, 23 Jul 2020 05:54:49 GMT
• Title: Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
• Authors: Zhengqin Li, Yu-Ying Yeh, Manmohan Chandraker
• Abstract summary: Complex light paths induced by refraction and reflection have prevented both traditional and deep multiview stereo from solving this problem. We propose a physically-based network to recover 3D shape of transparent objects using a few images acquired with a mobile phone camera. Our experiments show successful recovery of high-quality 3D geometry for complex transparent shapes using as few as 5-12 natural images.
• Score: 75.63464905190061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem. Complex light paths induced by refraction and reflection have prevented both traditional and deep multiview stereo from solving this challenge. We propose a physically-based network to recover 3D shape of transparent objects using a few images acquired with a mobile phone camera, under a known but arbitrary environment map. Our novel contributions include a normal representation that enables the network to model complex light transport through local computation, a rendering layer that models refractions and reflections, a cost volume specifically designed for normal refinement of transparent shapes and a feature mapping based on predicted normals for 3D point cloud reconstruction. We render a synthetic dataset to encourage the model to learn refractive light transport across different views. Our experiments show successful recovery of high-quality 3D geometry for complex transparent shapes using as few as 5-12 natural images. Code and data are publicly released.

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