Concave micro-mirror arrays fabricated as holographic optical elements are used in projector-based light field displays due to their see-through characteristics. The optical axes of each micro-mirror in the array are usually made parallel to each other, which simplifies the fabrication, integral image rendering, and calibration process. However, this demands that the beam from the projector be collimated and made parallel to the optical axis of each elemental micro-mirror. This requires additional collimation optics, which puts serious limitations on the size of the display. In this Letter, we propose a solution to the above issue by introducing a new method to fabricate holographic concave micro-mirror array sheets and explain how they work in detail. 3D light field reconstructions of the size $20\mathrm{cm}\times 10\mathrm{cm}$ and 6 cm in depth are achieved using a conventional projector without any collimation optics.

In this paper, we propose a novel, fully automatic method to obtain accurate view synthesis for soccer games. Existing methods often make assumptions about the scene. This usually requires manual input and introduces artifacts in situations not handled by those assumptions. Our method does not make assumptions about the scene; it solely relies on feature detection and utilizes the structures visible in a 3D light field to limit the search range of traditional view synthesis methods. A visual comparison between a standard plane sweep, a depth-aware plane sweep and our method is provided, showing that our method provides more accurate results in most cases.

Extended reality (XR) experiences are on the verge of becoming widely adopted in diverse application domains. An essential part of the technology is accurate tracking and localization of the headset to create an immersive experience. A subset of the applications require perfect co-location between the real and the virtual world, where virtual objects are aligned with real-world counterparts. Current headsets support co-location for small areas, but suffer from drift when scaling up to larger ones such as buildings or factories. This paper proposes tools and solutions for this challenge by splitting up the simultaneous localization and mapping (SLAM) into separate mapping and localization stages. In the pre-processing stage, a feature map is built for the entire tracking area. A global optimizer is applied to correct the deformations caused by drift, guided by a sparse set of ground truth markers in the point cloud of a laser scan. Optionally, further refinement is applied by matching features between the ground truth keyframe images and their rendered-out SLAM estimates of the point cloud. In the second, real-time stage, the rectified feature map is used to perform localization and sensor fusion between the global tracking and the headset. The results show that the approach achieves robust co-location between the virtual and the real 3D environment for large and complex tracking environments.

Free Viewpoint Television (FTV) is a new modality in next generation television, which provides the viewer free navigation through the scene, using image-based view synthesis from a couple of camera view inputs. The recently developed MPEG reference software technology is, however, restricted to narrow baselines and linear camera arrangements. Its reference software currently implements stereo matching and interpolation techniques, designed mainly to support three camera inputs (middle-left and middleright stereo). Especially in view of future use case scenarios in multi-scopic 3D displays, where hundreds of output views are generated from a limited number (tens) of wide baseline input views, it becomes mandatory to fully exploit all input camera information to its maximal potential. We therefore revisit existing view interpolation techniques to support dozens of camera inputs for better view synthesis performance. In particular, we show that Light Fields yield average PSNR gains of approximately 5 dB over MPEG's existing depth-based multiview video technology, even in the presence of large baselines.