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

Abstract We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed
to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog …

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades
by the computer vision, computer graphics, and machine learning communities. Since 2015 …

Convolutional networks for single-view object reconstruction have shown impressive
performance and have become a popular subject of research. All existing techniques are …

We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object
category models from collections of input images. In contrast to previous work, we are able …

We learn a self-supervised, single-view 3D reconstruction model that predicts the 3D mesh
shape, texture and camera pose of a target object with a collection of 2D images 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 …

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 proposes an uncalibrated photometric stereo method for non-Lambertian scenes
based on deep learning. Unlike previous approaches that heavily rely on assumptions of …

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 …