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.

本文针对近年来计算机视觉领域中关于自我中心图像物体分割技术的研究，受其在混合现实应用中的重要性所驱动，提供了一个基于 RGB-D THU-READ 数据集的 2124 个图像的语义级别标注，并使用 Thundernet 进行了基准测试，为未来相应的端到端混合现实应用提供了一定的参考价值。

实时自我中心物体分割：清华大学读取标签和基准测试结果

Real Time Egocentric Object Segmentation: THU-READ Labeling and  Benchmarking Results

In this study, we focus on the egocentric segmentation of arms to improve
self-perception in Augmented Virtuality (AV). The main contributions of this
work are: i) a comprehensive survey of segmentation algorithms for AV; ii) an
Egocentric Arm Segmentation Dataset, composed of more than 10, 000 images,
comprising variations of skin color, and gender, among others. We provide all
details required for the automated generation of groundtruth and semi-synthetic
images; iii) the use of deep learning for the first time for segmenting arms in
AV; iv) to showcase the usefulness of this database, we report results on
different real egocentric hand datasets, including GTEA Gaze+, EDSH, EgoHands,
Ego Youtube Hands, THU-Read, TEgO, FPAB, and Ego Gesture, which allow for
direct comparisons with existing approaches utilizing color or depth. Results
confirm the suitability of the EgoArm dataset for this task, achieving
improvement up to 40% with respect to the original network, depending on the
particular dataset. Results also suggest that, while approaches based on color
or depth can work in controlled conditions (lack of occlusion, uniform
lighting, only objects of interest in the near range, controlled background,
etc.), egocentric segmentation based on deep learning is more robust in real AV
applications.

本研究旨在通过自我虚拟环境中基于深度学习的以自我为中心手臂分割技术来改善自我感知，我们提供了一个多种皮肤颜色和性别变化的自我手臂分割数据集，并与使用颜色或深度的现有方法进行了直接比较，结果表明，该数据集的适用性达到了 40% 的提高率，同时深度学习的手臂分割方法具有更强的鲁棒性。