- Published on April 5, 2021
- In Deep Tech
Complete Guide to Neural Non-Rigid Tracking
Neural non-rigid tracking mechanism is robust in performance and cheaper to deploy in real-world object-tracking applications
- By Rajkumar Lakshmanamoorthy
Augmented Reality (AR) and Virtual Reality (VR) applications are growing enormously in the count. These applications rely chiefly on the reconstruction of 2D/3D images and scenes. Though there is consistent progress in capturing and reconstruction, this remains one of the challenging tasks in computer vision. Capturing and reconstructing static objects is performed with great accuracy via many architectures. However, capturing and reconstructing dynamic objects is still a domain that needs a solid development.
Dynamic object tracking and reconstruction are roughly classified into Rigid object tracking and reconstruction and Non-rigid object tracking and reconstruction. Rigid object tracking assumes a shape prior and tracks that predefined shape anywhere in the given frame. On the ot
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Rajkumar Lakshmanamoorthy
A geek in Machine Learning with a Master's degree in Engineering and a passion for writing and exploring new things. Loves reading novels, cooking, practicing martial arts, and occasionally writing novels and poems.
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