MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset
This work addresses 3D surface reconstruction for objects with challenging materials like metals and glass, representing an incremental advance in photometric stereo methods.
The authors tackled the photometric stereo problem by proposing a multi-scale network architecture and a new comprehensive synthetic training dataset, achieving state-of-the-art results with improved accuracy in estimated normal fields on public benchmarks.
The photometric stereo (PS) problem consists in reconstructing the 3D-surface of an object, thanks to a set of photographs taken under different lighting directions. In this paper, we propose a multi-scale architecture for PS which, combined with a new dataset, yields state-of-the-art results. Our proposed architecture is flexible: it permits to consider a variable number of images as well as variable image size without loss of performance. In addition, we define a set of constraints to allow the generation of a relevant synthetic dataset to train convolutional neural networks for the PS problem. Our proposed dataset is much larger than pre-existing ones, and contains many objects with challenging materials having anisotropic reflectance (e.g. metals, glass). We show on publicly available benchmarks that the combination of both these contributions drastically improves the accuracy of the estimated normal field, in comparison with previous state-of-the-art methods.