Real Time Egocentric Object Segmentation: THU-READ Labeling and Benchmarking Results
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingMixed RealityReal-Time Semantic SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Enhanced Self-Perception in Mixed Reality: Egocentric Arm Segmentation and Database with Automatic Labelling
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…
Mixed RealitySegmentationEgoFun3D: Modeling Interactive Objects from Egocentric Videos using Function Templates
We present EgoFun3D, a coordinated task formulation, dataset, and benchmark for modeling interactive 3D objects from egocentric videos. Interactive objects are of high interest for embodied AI but scarce, making modeling…
Real Time Egocentric Segmentation for Video-self Avatar in Mixed Reality
In this work we present our real-time egocentric body segmentation algorithm. Our algorithm achieves a frame rate of 66 fps for an input resolution of 640x480, thanks to our shallow network inspired in Thundernet's archi…
Mixed RealitySegmentationSemantic SegmentationEgocentric Human Segmentation for Mixed Reality
The objective of this work is to segment human body parts from egocentric video using semantic segmentation networks. Our contribution is two-fold: i) we create a semi-synthetic dataset composed of more than 15, 000 real…
Mixed RealitySegmentationSemantic SegmentationActionVOS: Actions as Prompts for Video Object Segmentation
Delving into the realm of egocentric vision, the advancement of referring video object segmentation (RVOS) stands as pivotal in understanding human activities. However, existing RVOS task primarily relies on static attri…
ObjectReferring Video Object SegmentationSegmentationSemantic Segmentation+2