Neural detection of spheres in images for lighting calibration

Abstract

Accurate detection of spheres in images holds significant value for photometric 3D vision techniques such as photometric stereo.1 These techniques require precise calibration of lighting, and sphere detection can help in the calibration process. Our proposed approach involves training neural networks to automatically detect spheres of three different material classes: matte, shiny and chrome. We get fast and accurate segmentation of spheres in images, outperforming manual segmentation in terms of speed while maintaining comparable accuracy.