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 …

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 …

Reconstructing the 3D shape of an object using several images under different light sources
is a very challenging task, especially when realistic assumptions such as light propagation …

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

This paper presents a simple and effective solution to the longstanding classical multi-view
photometric stereo (MVPS) problem. It is well-known that photometric stereo (PS) is …

This paper presents an uncalibrated deep neural network framework for the photometric
stereo problem. For training models to solve the problem, existing neural network-based …

This paper proposes a practical photometric solution for the challenging problem of in-the-
wild inverse rendering under unknown ambient lighting. Our system recovers scene …

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 …

We present a modern solution to the multi-view photometric stereo problem (MVPS). Our
work suitably exploits the image formation model in a MVPS experimental setup to recover …

Convolutional neural networks (CNNs) provide the best accuracy for disparity estimation.
However, CNNs are computationally expensive, making them unfavorable for resource …