MD Raqib Khan

Project Title

Stereo matching and depth estimation are crucial tasks in 3-D reconstruction and autonomous driving. The existing deep-learning approaches gain remarkable performance over traditional pipelines. These approaches have improved performance on difficult depth estimation datasets but have limited generalized performance. Further, advanced computer vision applications such as augmented reality and virtual reality demand real-time performance. In order to achieve this and overcome the limitations of existing learning-based approaches, this project will involve the design and development of learning based methods for stereo matching and depth estimation, with the goal of developing lightweight deep learning models for real-time depth estimation for AR, VR and robotics applications.

Supervision Team

Stereo matching and depth estimation are crucial tasks in 3-D reconstruction and autonomous driving. The existing deep-learning approaches gain remarkable performance over traditional pipelines. These approaches have improved performance on difficult depth estimation datasets but have limited generalized performance. Further, advanced computer vision applications such as augmented reality and virtual reality demand real-time performance. In order to achieve this and overcome the limitations of existing learning-based approaches, this project will involve the design and development of learning based methods for stereo matching and depth estimation, with the goal of developing lightweight deep learning models for real-time depth estimation for AR, VR and robotics applications.

Description

Stereo matching and depth estimation are crucial tasks in 3-D reconstruction and autonomous driving. The existing deep-learning approaches gain remarkable performance over traditional pipelines. These approaches have improved performance on difficult depth estimation datasets but have limited generalized performance. Further, advanced computer vision applications such as augmented reality and virtual reality demand real-time performance. In order to achieve this and overcome the limitations of existing learning-based approaches, this project will involve the design and development of learning based methods for stereo matching and depth estimation, with the goal of developing lightweight deep learning models for real-time depth estimation for AR, VR and robotics applications.