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In this work we propose a novel, highly practical, binocular photometricstereo (PS) framework, which has same acquisition speed as single view PS,however significantly improves the quality of the estimated geometry.  As in recent neural multi-view shape estimation frameworks such as NeRF,SIREN and inverse graphics approaches to multi-view photometric stereo (e.g.PS-NeRF) we formulate shape estimation task as learning of a differentiablesurface and texture representation by minimising surface normal discrepancy fornormals estimated from multiple varying light images for two views as well asdiscrepancy between rendered surface intensity and observed images. Our methoddiffers from typical multi-view shape estimation approaches in two key ways.First, our surface is represented not as a volume but as a neural heightmapwhere heights of points on a surface are computed by a deep neural network.Second, instead of predicting an average intensity as PS-NeRF or introducinglambertian material assumptions as Guo et al., we use a learnt BRDF and performnear-field per point intensity rendering.  Our method achieves the state-of-the-art performance on the DiLiGenT-MVdataset adapted to binocular stereo setup as well as a new binocularphotometric stereo dataset - LUCES-ST.

A Neural Height-Map Approach for the Binocular Photometric Stereo Problem