  The representation of consistent mixed reality (XR) environments requires
adequate real and virtual illumination composition in real-time. Estimating the
lighting of a real scenario is still a challenge. Due to the ill-posed nature
of the problem, classical inverse-rendering techniques tackle the problem for
simple lighting setups. However, those assumptions do not satisfy the current
state-of-art in computer graphics and XR applications. While many recent works
solve the problem using machine learning techniques to estimate the environment
light and scene's materials, most of them are limited to geometry or previous
knowledge. This paper presents a CNN-based model to estimate complex lighting
for mixed reality environments with no previous information about the scene. We
model the environment illumination using a set of spherical harmonics (SH)
environment lighting, capable of efficiently represent area lighting. We
propose a new CNN architecture that inputs an RGB image and recognizes, in
real-time, the environment lighting. Unlike previous CNN-based lighting
estimation methods, we propose using a highly optimized deep neural network
architecture, with a reduced number of parameters, that can learn high complex
lighting scenarios from real-world high-dynamic-range (HDR) environment images.
We show in the experiments that the CNN architecture can predict the
environment lighting with an average mean squared error (MSE) of \num{7.85e-04}
when comparing SH lighting coefficients. We validate our model in a variety of
mixed reality scenarios. Furthermore, we present qualitative results comparing
relights of real-world scenes.
