Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HDR Environment Map Estimation for Real-Time Augmented Reality   We present a method to estimate an HDR environment map from a narrow
field-of-view LDR camera image in real-time. This enables perceptually
appealing reflections and shading on virtual objects of any material finish,
from mirror to diffuse, rendered into a real physical environment using
augmented reality. Our method is based on our efficient convolutional neural
network architecture, EnvMapNet, trained end-to-end with two novel losses,
ProjectionLoss for the generated image, and ClusterLoss for adversarial
training. Through qualitative and quantitative comparison to state-of-the-art
methods, we demonstrate that our algorithm reduces the directional error of
estimated light sources by more than 50%, and achieves 3.7 times lower Frechet
Inception Distance (FID). We further showcase a mobile application that is able
to run our neural network model in under 9 ms on an iPhone XS, and render in
real-time, visually coherent virtual objects in previously unseen real-world
environments.

20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex