  Egocentric segmentation has attracted recent interest in the computer vision
community due to their potential in Mixed Reality (MR) applications. While most
previous works have been focused on segmenting egocentric human body parts
(mainly hands), little attention has been given to egocentric objects. Due to
the lack of datasets of pixel-wise annotations of egocentric objects, in this
paper we contribute with a semantic-wise labeling of a subset of 2124 images
from the RGB-D THU-READ Dataset. We also report benchmarking results using
Thundernet, a real-time semantic segmentation network, that could allow future
integration with end-to-end MR applications.
