Inverse Rendering of Translucent Objects using Physical and Neural Renderers

• URL: http://arxiv.org/abs/2305.08336v1
• Date: Mon, 15 May 2023 04:03:11 GMT
• Title: Inverse Rendering of Translucent Objects using Physical and Neural Renderers
• Authors: Chenhao Li, Trung Thanh Ngo, Hajime Nagahara
• Abstract summary: In this work, we propose an inverse model that estimates 3D shape, spatially-varying reflectance, homogeneous scattering parameters, and an environment illumination jointly from only a pair of captured images of a translucent object. Because two reconstructions are differentiable, we can compute a reconstruction loss to assist parameter estimation. We constructed a large-scale synthetic dataset of translucent objects, which consists of 117K scenes.
• Score: 13.706425832518093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose an inverse rendering model that estimates 3D shape, spatially-varying reflectance, homogeneous subsurface scattering parameters, and an environment illumination jointly from only a pair of captured images of a translucent object. In order to solve the ambiguity problem of inverse rendering, we use a physically-based renderer and a neural renderer for scene reconstruction and material editing. Because two renderers are differentiable, we can compute a reconstruction loss to assist parameter estimation. To enhance the supervision of the proposed neural renderer, we also propose an augmented loss. In addition, we use a flash and no-flash image pair as the input. To supervise the training, we constructed a large-scale synthetic dataset of translucent objects, which consists of 117K scenes. Qualitative and quantitative results on both synthetic and real-world datasets demonstrated the effectiveness of the proposed model.

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