Light Direction and Color Estimation from Single Image with Deep Regression
This addresses a computer vision problem for applications like augmented reality, but it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of estimating light direction and color from a single image by using a new synthetic dataset and a deep architecture, achieving good performance on both synthetic and real scenes.
We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes.