arXiv:2203.07904v3 Announce Type: replace-cross Abstract: We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.
Advances in deep learning and computational photography are converging to enable new methods for passive 3D reconstruction foundational to robotics and augmented reality applications.
Improved depth estimation from standard cameras, especially using unsupervised methods, reduces reliance on specialized hardware and improves the accuracy and robustness of AI perception systems.
The ability to accurately estimate depth from focal stack images without ground truth data expands the utility of existing camera hardware for 3D sensing tasks in real-world environments.
- · Machine Vision companies
- · Robotics sector
- · Augmented Reality developers
- · AI hardware manufacturers
- · Specialized depth sensor manufacturers
- · Companies reliant on expensive 3D scanning solutions
More accurate and cost-effective 3D environmental understanding becomes available for various applications.
This could accelerate the development and deployment of autonomous systems and sophisticated human-computer interfaces.
Widespread, high-fidelity environmental mapping might enable new forms of digital twins and pervasive computational control over physical spaces.
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Read at arXiv cs.LG