With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …

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 …

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 …

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

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 …

We present a new data-driven approach with physics-based priors to scene-level normal
estimation from a single polarization image. Existing shape from polarization (SfP) works …

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 …

We introduce a novel learning-based method to reconstruct the high-quality geometry and
complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images …