Most of tomorrow's audio-visual formats increase the feeling of immersion in the content. The challenge is to transform the end user's experience by immersing him in the heart of the action or information. At the same time, computer vision technologies (augmented reality, virtual reality, volumetric video, etc.) are fed by progress in artificial intelligence. They are revolutionizing many industrial sectors by offering a new perception of the environment.
The Image Vision & Immersion laboratory builds tools that enable the adoption of these new technologies by professionals and the general public. It delivers expertise and technological bricks related to the characteristics and dimensions of the image (resolution, colorimetry, exposure, segmentation, compression...), sound (spatialization), and new rendering devices (screen, projection, headsets, and glasses for AR/VR in 3 or 6 degrees of freedom, holography...). These technologies allow the company to address different fields of application, from industry to medicine (in particular with industrial holography, augmented reality for health, etc.), from entertainment (television, cinema, etc.) to serious gaming (training, institutional, etc.), by placing the user at the heart of its thinking and its work, from the definition of needs to the use of the proposed solutions.
The progress offered by new technologies, thanks to artificial intelligence, opens up unsuspected prospects for innovation in all fields that use image or sound, environmental modeling, or rendering from 2D to extended reality.
03.22.2023
Digital reconstructions of numerical holograms enable data visualization and serve a multitude of purposes ranging from microscopy to holographic displays. Over the years, many pipelines have been developed for specific hologram types. Within the standardization effort of JPEG Pleno holography, an open-source MATLAB toolbox was developed that reflects the best current consensus. It can process…
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02.20.2023
Automatic depth map retrieval from digital holograms using a deep learning approach
Information extraction from computer-generated holograms using learning-based methods is a topic that has not received much research attention. In this article, we propose and study two learning-based methods to extract the depth information from a hologram and compare their performance with that of classical depth from focus (DFF) methods. We discuss the main characteristics of a hologram and…
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02.21.2023
Automatic depth map retrieval from digital holograms using a depth-from-focus approach
Recovering the scene depth map from a computer-generated hologram is a problem that remains unsolved, despite the growing interest in the subject. In this article, we propose to study the application of depth-from-focus (DFF) methods to retrieve the depth information from the hologram. We discuss the different hyperparameters that are required for the application of the method and their impact on…
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02.20.2023
PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
Object pre-localization from computer-generated holograms is still an open problem in the current state of the art. In this work, we propose the use of the hologram phase space representation to determine a set of regions of interest where the searched object can be located. The extracted regions can be used to pre-locate the object in 3D space and are further refined to produce a more accurate…
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2019
Product of the Year at NAB Show de Las Vegas
2017
Technology Innovation Award at NAB Show de Las Vegas
