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

Neural-network-based single image depth prediction (SIDP) is a challenging task where the
goal is to predict the scene's per-pixel depth at test time. Since the problem, by definition, is …

We introduce VA-DepthNet, a simple, effective, and accurate deep neural network approach
for the single-image depth prediction (SIDP) problem. The proposed approach advocates …

We propose a fast and generalizable solution to Multiview Photometric Stereo (MVPS),
called MVPSNet. The key to our approach is a feature extraction network that effectively …

Image-based 3D reconstruction has been employed in industrial metrology for micro-
measurements and quality control purposes. However, generating a highly-detailed and …

Following the advent of NeRFs, 3D Gaussian Splatting (3D-GS) has paved the way to real-
time neural rendering overcoming the computational burden of volumetric methods. Several …

Human modeling and relighting are two fundamental problems in computer vision and
graphics, where high-quality datasets can largely facilitate related research. However, most …

Multi-view photometric stereo (MVPS) is a preferred method for detailed and precise 3D
acquisition of an object from images. Although popular methods for MVPS can provide …