LIGHTS: LIGHT SPECULARITY DATASET FOR SPECULAR DETECTION IN MULTI-VIEW

Specular highlights are commonplace in images, however, methods for detecting them and removing the phenomenon are particularly challenging. A reason for this is the difficulty in creating a dataset for training or evaluation, as in the real world, we lack the necessary control over the environment. Therefore, we propose a novel physically-based rendered LIGHT Specularity (LIGHTS) Dataset for the evaluation of the specular highlight detection task. Our dataset consists of 18 high-quality architectural scenes, where each scene is rendered with multiple views. In total, the dataset contains 2; 603 views with an average of 145 views per scene. Additionally, we propose a simple aggregation based method for specular highlight detection that outperforms prior work by 3:6% in two orders of magnitude less time on our dataset.