Recent progress on photometric stereo extends the technique to deal with general materials
and unknown illumination conditions. However, due to the lack of suitable benchmark data …

Evaluating photometric stereo using real-world dataset is important yet difficult. Existing
datasets are insufficient due to their limited scale and random distributions in shape and …

This paper tackles a new photometric stereo task, named universal photometric stereo.
Unlike existing tasks that assumed specific physical lighting models; hence, drastically …

This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes
based on deep learning. Unlike previous approaches that heavily rely on assumptions of …

This paper presents a photometric stereo method based on deep learning. One of the major
difficulties in photometric stereo is designing an appropriate reflectance model that is both …

Photometric stereo estimates the surface normal given a set of images acquired under
different illumination conditions. To deal with diverse factors involved in the image formation …

This paper addresses the problem of photometric stereo, in both calibrated and uncalibrated
scenarios, for non-Lambertian surfaces based on deep learning. We first introduce a fully …

This paper addresses the problem of photometric stereo for non-Lambertian surfaces.
Existing approaches often adopt simplified reflectance models to make the problem more …

The seminal multiple view stereo benchmark evaluations from Middlebury and by Strecha et
al. have played a major role in propelling the development of multi-view stereopsis …

Photometric stereo recovers three-dimensional (3D) object surface normal from multiple
images under different illumination directions. Traditional photometric stereo methods suffer …