Depth from focus is a simple and effective methodology for retrieving the scene geometry from a hologram when used with the appropriate focus measure and patch size. However, fixing those parameters for every sample may not be the right choice, as different scenes can be composed with various types of textures. In this work, we propose a self-supervised learning methodology for fusing the depth maps produced using different focus measures with variable patch sizes applied to the holographic reconstruction volume. Experimental results show that fusing depth information produces more accurate and smoother depth maps, which can be directly used for alternative tasks such as motion estimation.

Despite the growing interest for Holography, there is a lack of publicly available three-dimensional hologram sequences for the evaluation of video codecs with inter-frame compression mechanisms such as motion estimation and compensation. In this paper, we report the first large-scale dataset containing 18 holographic videos computed with three different resolutions and pixel pitches. By providing the color and depth images corresponding to each hologram frame, our dataset can be used in additional applications such as the validation of 3D scene geometry retrieval or deep learning-based hologram synthesis methods. Altogether, our dataset comprises 5400 pairs of RGB-D images and holograms, totaling more than 550 GB of data.

We present a wearable full-color holographic augmented reality headset with binocular vision support and real-time embedded hologram calculation. Contrarily to most previously proposed prototypes, our headset employs high-speed amplitude-only microdisplays and embeds a compact and lightweight electronic board to drive and synchronize the microdisplays and light source engines. In addition, to enable a standalone usage of the headset, we developed a real-time hologram rendering engine capable of computing full-color binocular holograms at over 35 frames per second on a NVIDIA Jetson AGX Orin embedded platform. Finally, we provide a comparison of the efficiency of laser diodes and superluminescent diodes for the reduction of speckle noise, which greatly affects the reconstructed image’s quality. Experimental results show that our prototype enables full-color holographic images to be reconstructed with accurate focus cues and reduced speckle noise in real-time.

At its 93rd meeting, JPEG received the responses to the Call for Proposals on JPEG Pleno holography. This activity will lead to the first standardized coding solution for digital holograms. This paper describes the quality evaluation process, existing out of a subjective evaluation study and an objective evaluation based on preselected quality metrics. It also includes a critical evaluation of the challenges encountered when performing quality evaluation of holographic content. Based on the evaluation of the response, the JPEG committee was able to select a very reliable coding solution that depicted a performance well beyond that of the selected anchors.

Thanks to its ability to provide accurate focus cues, Holography is considered as a promising display technology for augmented reality glasses. However, since it contains a large amount of data, the calculation of a hologram is a time-consuming process which results in prohibiting head-motion-to-photon latency, especially when using embedded calculation hardware. In this paper, we present a real-time hologram calculation method implemented on a NVIDIA Jetson AGX Xavier embedded platform. Our method is based on two modules: an offline pre-computation module and an on-the-fly hologram synthesis module. In the offline calculation module, the omnidirectional light field scattered by each scene object is individually pre-computed and stored in a Look-Up Table (LUT). Then, in the hologram synthesis module, the light waves corresponding to the viewer's position and orientation are extracted from the LUT in real-time to compute the hologram. Experimental results show that the proposed method is able to compute 2K1K color holograms at more than 50 frames per second, enabling its use in augmented reality applications.

Unlike conventional images and videos, digital holograms contain large amounts of data with very low redundancy. Consequently, current communication networks may be not able to meet the bandwidth requirements for hologram transmission in reasonable time. To enable practical streaming of holographic contents, we propose a progressive coding method that combines quality scalability with viewpoint scalability. From a Gabor wavelets decomposition of the hologram, the server starts by selecting the coefficients corresponding to the user's viewpoint. Then, the selected coefficients are encoded progressively according to their importance for the reconstructed view. Experimental results reveal that our approach outperforms conventional scalable codecs and enables the streaming of holographic data with a better quality of experience.

We propose a fast Computer-Generated Hologram (CGH) computation method based on a hybrid point-source/wave-field approach. Whereas previously proposed methods tried to reduce the computational complexity of the point-source or the wave-field approaches independently, our method uses the two approaches together and therefore takes advantages from both of them. The algorithm consists of three steps. First, the 3D scene is sliced into several depth layers parallel to the hologram plane. Then, for each layer, we compute the complex wave scattered by this layer either using a wave-field or a point-source approach according to a threshold criterion on the number of points within the layer. Finally, we sum up the complex waves scattered by all the depth layers in order to obtain the final CGH. Experimental results reveal that this combination of approaches does not produce any visible artifact and outperforms both the point-source and wave-field approaches.