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Augmented Reality Semantic Progress Tracking
In this project, we aim to develop an online system that detects installations as defined in a digital plan using onboard sensing of an AR system (e.g., camera sensors) and track their completion.
Keywords: Augmented Reality, Computer Vision, Semantic detection, Pose tracking, Mapping, Digital Construction
Augmented Reality has the potential to drastically change the way we interact with the world. In building construction for instance, AR technology has the potential to render manual measuring equipment and printed plans obsolete. One common task is tracking progress of a construction site and record the its state over the course of time.
In this project, we aim to develop an online system that detects installations as defined in a digital plan using onboard sensing of an AR system (e.g., camera sensors) and track their completion. The technological challenges in this project are extracting semantic data from both 3D plans and real-world perception data and relating them in a robust fashion.
Augmented Reality has the potential to drastically change the way we interact with the world. In building construction for instance, AR technology has the potential to render manual measuring equipment and printed plans obsolete. One common task is tracking progress of a construction site and record the its state over the course of time.
In this project, we aim to develop an online system that detects installations as defined in a digital plan using onboard sensing of an AR system (e.g., camera sensors) and track their completion. The technological challenges in this project are extracting semantic data from both 3D plans and real-world perception data and relating them in a robust fashion.
- Familiarize yourself with the current state of the art.
- Investigate existing solutions for cross-modality semantic object tracking.
- Develop novel algorithms to address the challenges of tracking progress on construction sites.
- Test your algorithms on evaluation data, and real AR systems.
- Familiarize yourself with the current state of the art. - Investigate existing solutions for cross-modality semantic object tracking. - Develop novel algorithms to address the challenges of tracking progress on construction sites. - Test your algorithms on evaluation data, and real AR systems.
- Good understanding of algorithmic challenges.
- Knowledge of C++ is mandatory, Python is recommended.
- Knowledge of ROS is recommended.
- Knowledge in two of the following areas: SLAM, Localization, sensor fusion, computer vision, deep learning.
- Be curious about pushing the limits of today's Augmented Reality capabilities.
- Strong self-motivation and critical mind.
- Students from outside of D-MAVT (particularly, from D-INFK, D-ITET, D-PHYS, and D-MATH) are also highly encouraged to apply.
- Good understanding of algorithmic challenges. - Knowledge of C++ is mandatory, Python is recommended. - Knowledge of ROS is recommended. - Knowledge in two of the following areas: SLAM, Localization, sensor fusion, computer vision, deep learning. - Be curious about pushing the limits of today's Augmented Reality capabilities. - Strong self-motivation and critical mind. - Students from outside of D-MAVT (particularly, from D-INFK, D-ITET, D-PHYS, and D-MATH) are also highly encouraged to apply.
Please send your cv and transcripts to: Abel Gawel (gawela@ethz.ch), Fadri Furrer (fadri@ethz.ch), and Timothy Sandy (tsandy@ethz.ch). Also note that new openings are only available from March 2021.
Please send your cv and transcripts to: Abel Gawel (gawela@ethz.ch), Fadri Furrer (fadri@ethz.ch), and Timothy Sandy (tsandy@ethz.ch). Also note that new openings are only available from March 2021.