Learning scene and blur model for active chromatic depth from defocus
| Autor: | Frédéric Champagnat, Benjamin Buat, Guy Le Besnerais, Pauline Trouvé-Peloux |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Monocular
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Covariance Atomic and Molecular Physics and Optics law.invention Lens (optics) Computer Science::Graphics Optics Projector law Computer Science::Computer Vision and Pattern Recognition Chromatic aberration Calibration Computer vision Chromatic scale Artificial intelligence Electrical and Electronic Engineering Projection (set theory) business Engineering (miscellaneous) ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Applied optics. 60(31) |
| ISSN: | 1539-4522 |
| Popis: | In this paper, we propose what we believe is a new monocular depth estimation algorithm based on local estimation of defocus blur, an approach referred to as depth from defocus (DFD). Using a limited set of calibration images, we directly learn image covariance, which encodes both scene and blur (i.e., depth) information. Depth is then estimated from a single image patch using a maximum likelihood criterion defined using the learned covariance. This method is applied here within a new active DFD method using a dense textured projection and a chromatic lens for image acquisition. The projector adds texture for low-textured objects, which is usually a limitation of DFD, and the chromatic aberration increases the estimated depth range with respect to a conventional DFD. Here, we provide quantitative evaluations of the depth estimation performance of our method on simulated and real data of fronto-parallel untextured scenes. The proposed method is then experimentally evaluated qualitatively using a 3D printed benchmark. |
| Databáze: | OpenAIRE |
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