3D Objects Reconstruction Using Frontal Images. An Example With Guitars

The effects of strong color casts in traditional, learning-based and data-driven color constancy algorithms is analyzed. This is the first study investigating the response of color constancy methods to illuminants on the edges and outside the color temperature curve. According to the comprehensive experiments, while traditional studies do not fail to "discount the illuminant" from inputs which have strong color casts, the efficiency of learning-based and data-driven algorithms in obtaining canonical outputs decreases significantly compared to traditional methods. We discuss the reasons behind this performance decay and introduce a traditional color constancy algorithm, which presents competitive results in a challenging dataset.