arXiv:1806.09316
Vision-based Pose Estimation for Augmented Reality : A Comparison Study
원문 초록
Augmented reality aims to enrich our real world by inserting 3D virtual objects. In order to accomplish this goal, it is important that virtual elements are rendered and aligned in the real scene in an accurate and visually acceptable way. The solution of this problem can be related to a pose estimation and 3D camera localization. This paper presents a survey on different approaches of 3D pose estimation in augmented reality and gives classification of key-points-based techniques. The study given in this paper may help both developers and researchers in the field of augmented reality.
함께 살펴볼 논문
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VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation
PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation
CoMotion: Concurrent Multi-person 3D Motion
Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks
Automating 3D Dataset Generation with Neural Radiance Fields
AthleticsPose: Authentic Sports Motion Dataset on Athletic Field and Evaluation of Monocular 3D Pose Estimation Ability
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