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

arXiv: https://arxiv.org/abs/2106.04957 · HTML: https://mlsanity.com/paper/2106.04957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations](https://mlsanity.com/paper/1704.02463/) (2017)
- [Enhanced Self-Perception in Mixed Reality: Egocentric Arm Segmentation and Database with Automatic Labelling](https://mlsanity.com/paper/2003.12352/) (2020)
- [Egocentric Human Segmentation for Mixed Reality](https://mlsanity.com/paper/2005.12074/) (2020)

## Built on by (most cited first, 1 of 1)

- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)

## Abstract (arXiv)

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.