Photometric stereo recovers the surface normals of an object from multiple images with
varying shading cues, ie, modeling the relationship between surface orientation and …

Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple
disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural …

Photometric stereo recovers the surface normals of an object from multiple images with
varying shading cues, ie, modeling the relationship between surface orientation and …

Photometric stereo aims to recover the surface normals of a 3D object from various shading
cues, establishing the relationship between two-dimensional images and the object …

This paper presents a critical analysis of image-based 3D reconstruction using neural
radiance fields (NeRFs), with a focus on quantitative comparisons with respect to traditional …

Acquiring high-resolution 3D surface structures is a crucial task in computer vision as it
provides more detailed surface textures and clearer structures. Photometric stereo can …

In this paper, we introduce SDM-UniPS, a groundbreaking Scalable, Detailed, Mask-free,
and Universal Photometric Stereo network. Our approach can recover astonishingly intricate …

In this paper, we address the" dual problem" of multi-view scene reconstruction in which we
utilize single-view images captured under different point lights to learn a neural scene …

This paper tackles the task of uncalibrated photometric stereo for 3D object reconstruction,
where both the object shape, object reflectance, and lighting directions are unknown. This is …

This paper aims at recovering the shape of a scene with unknown, non-Lambertian, and
possibly spatially-varying surface materials. When the shape of the object is highly complex …