Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LIGHTS: LIGHT Specularity Dataset for specular detection in Multi-view   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.

26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio