SPLINE-Net: Sparse photometric stereo through lighting interpolation and normal estimation networks
Proceedings of the IEEE/CVF International Conference on …, 2019•openaccess.thecvf.com
This paper solves the Sparse Photometric stereo through Lighting Interpolation and Normal
Estimation using a generative Network (SPLINE-Net). SPLINE-Net contains a lighting
interpolation network to generate dense lighting observations given a sparse set of lights as
inputs followed by a normal estimation network to estimate surface normals. Both networks
are jointly constrained by the proposed symmetric and asymmetric loss functions to enforce
isotropic constrain and perform outlier rejection of global illumination effects. SPLINE-Net is …
Estimation using a generative Network (SPLINE-Net). SPLINE-Net contains a lighting
interpolation network to generate dense lighting observations given a sparse set of lights as
inputs followed by a normal estimation network to estimate surface normals. Both networks
are jointly constrained by the proposed symmetric and asymmetric loss functions to enforce
isotropic constrain and perform outlier rejection of global illumination effects. SPLINE-Net is …
Abstract
This paper solves the Sparse Photometric stereo through Lighting Interpolation and Normal Estimation using a generative Network (SPLINE-Net). SPLINE-Net contains a lighting interpolation network to generate dense lighting observations given a sparse set of lights as inputs followed by a normal estimation network to estimate surface normals. Both networks are jointly constrained by the proposed symmetric and asymmetric loss functions to enforce isotropic constrain and perform outlier rejection of global illumination effects. SPLINE-Net is verified to outperform existing methods for photometric stereo of general BRDFs by using only ten images of different lights instead of using nearly one hundred images.
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