This paper presents a near-light photometric stereo method for spatially varying
reflectances. Recent studies in photometric stereo proposed learning-based approaches to …

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 …

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

Current non-Lambertian photometric stereo methods generally require a large number of
images to ensure accurate surface normal estimation. To achieve accurate surface normal …

Estimating 3D surface normals through photometric stereo has been of great interest in
computer vision research. Despite the success of existing traditional and deep learning …

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

This article 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 recovers the surface normals of an object from images with varying
shading cues. Conventional photometric stereo methods attempt to use handcrafted …

Retrieving accurate 3D reconstructions of objects from the way they reflect light is a very
challenging task in computer vision. Despite more than four decades since the definition of …