Abstract
In this paper, we present Point Cloud Color Constancy, in short PCCC, an
illumination chromaticity estimation algorithm exploiting a point cloud. We
leverage the depth information captured by the time-of-flight (ToF) sensor
mounted rigidly with the RGB sensor, and form a 6D cloud where each point
contains the coordinates and RGB intensities, noted as (x,y,z,r,g,b). PCCC
applies the PointNet architecture to the color constancy problem, deriving the
illumination vector point-wise and then making a global decision about the
global illumination chromaticity. On two popular RGB-D datasets, which we
extend with illumination information, as well as on a novel benchmark, PCCC
obtains lower error than the state-of-the-art algorithms. Our method is simple
and fast, requiring merely 16*16-size input and reaching speed over 500 fps,
including the cost of building the point cloud and net inference.