  Specular highlights are commonplace in images, however, methods for detecting
them and in turn removing the phenomenon are particularly challenging. A reason
for this, is due to the difficulty of 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 we have 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.
