Three-dimensional reconstruction of objects from shading information is a
challenging task in computer vision. As most of the approaches facing the
Photometric Stereo problem use simplified far-field assumptions, real-world
scenarios have essentially more complex physical effects that need to be
handled for accurately reconstructing the 3D shape. An increasing number of
methods have been proposed to address the problem when point light sources are
assumed to be nearby the target object. The proximity of the light sources
complicates the modeling of the image formation as the light behaviour requires
non-linear parameterisation to describe its propagation and attenuation.
To understand the capability of the approaches dealing with this near-field
scenario, the literature till now has used synthetically rendered photometric
images or minimal and very customised real-world data. In order to fill the gap
in evaluating near-field photometric stereo methods, we introduce LUCES the
first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo'
of 14 objects of a varying of materials. A device counting 52 LEDs has been
designed to lit each object positioned 10 to 30 centimeters away from the
camera. Together with the raw images, in order to evaluate the 3D
reconstructions, the dataset includes both normal and depth maps for comparing
different features of the retrieved 3D geometry. Furthermore, we evaluate the
performance of the latest near-field Photometric Stereo algorithms on the
proposed dataset to assess the SOTA method with respect to actual close range
effects and object materials.

提出了第一个 LUCES 数据集，包括 52 个 LED 灯，对 14 个物体进行了近距离照明，并用于评估最新的近场物体光度立体算法的性能，以评估最新的近场光度立体算法在实际物体材料的近距离效果方面的表现。