Depth reconstruction by using deep learning stereo matching algorithms
Problem statement
Nowadays, applying cameras is becoming more and more common; especially because these cameras are becoming cheaper and easier to use. Also, the demand for cameras and automation is increasing in multiple sectors. Due to the fact that cameras are becoming cheaper and more common, different suppliers of cameras came up with each their own characteristics, where some cameras are even able to capture both RGB and depth images by using infrared or stereo matching for example. Nevertheless, the implemented stereo matching algorithms differ in accuracy by comparing for example the StereoLabs ZED 2i and the OAK-D cameras. This accuracy may depend on the stereo matching algorithm applied. Therefore, it is interesting to derive the depth ourselves by using deep learning stereo matching algorithms.
Task
The task of this project is to investigate how depth could be reconstructed based on two adjacent images collected by a stereo camera. For this investigation, several aspects are important, such as the depth accuracy and the calculation speed. Furthermore, aspects such as the maximum depth, advantages, and disadvantages per algorithm are important to consider too. At the end of the project, a report needs to be written that clearly explains the different algorithms with their challenges and the differences between the stereo cameras used in the project. Your results will be used for a possible new stereo camera developed by Track32!
Work
20% Theory, 10% data collection, 50% implementation, 20% writing report
Contact:
Le Viet Duc, v.d.le@utwente.nl
About Track32:
At Track32 we produce innovative computer vision and AI software. Making the technology easily accessible, so that it becomes part of your organization’s natural intelligence.
Track32 provides state-of-the-art computer vision and AI algorithms, and we integrate them into existing or new hardware and software systems. We are experts in processing all types of visual and non-visual data, using deep learning and other methods. Track32 excels at analyzing the user’s requirements and matching them with technically feasible and cost-effective solutions. Using our computer vision and AI solutions leads to huge cost savings and massively increased operational efficiency for our customers.
Computer vision and AI are generic technologies that can be used in any application domain. We serve a wide spectrum of customers in the agricultural supply chain, but also players in other markets such as post harvest, material handling, spatial planning, healthcare and the life sciences. We serve commercial companies as well as research institutes and government.