Recent progress on photometric stereo extends the technique to deal with general materials
and unknown illumination conditions. However, due to the lack of suitable benchmark data …

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

We show how to train a fully convolutional neural network to perform inverse rendering from
a single, uncontrolled image. The network takes an RGB image as input, regresses albedo …

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 …

Photometric stereo aims to recover detailed surface shapes from images captured under
varying illuminations. However, existing real-world datasets primarily focus on evaluating …

Reconstructing the shape of an object from images is an important problem in computer
vision that has led to a variety of solution strategies. This survey covers photometric stereo …

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

This paper solves the Sparse Photometric stereo through Lighting Interpolation and Normal
Estimation using a generative Network (SPLINE-Net). SPLINE-Net contains a lighting …

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought
by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the …

We present the first large scale system for capturing and rendering relight able scene
reconstructions from massive unstructured photo collections taken under different …