A CNN Based Approach for the Point-Light Photometric Stereo Problem

TL;DR

This paper introduces a CNN-based method for realistic point-light Photometric Stereo that accounts for complex lighting effects, outperforming existing methods on real-world datasets.

Contribution

The work presents a novel iterative CNN approach for near-field point-light PS that models specular reflection, global illumination, and perspective effects, advancing beyond prior relaxations.

Findings

Outperforms state-of-the-art on DiLiGenT dataset

Introduces LUCES, first near-field PS dataset with 14 objects

Achieves superior results on real-world near-field data

Abstract

Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to the point light setup. We achieve this by employing an iterative procedure of point-light PS for shape estimation which has two main steps. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. Our approach sigificantly outperforms the state-of-the-art on the DiLiGenT real world dataset. Furthermore, in order to measure the performance of our approach for near-field point-light source PS data, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of different materials were the effects of point light sources and perspective viewing are a lot more significant. Our approach also outperforms the competition on this dataset as well. Data and test code are available at the project page.