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With the ever-increasing amount of 3D data being captured and processed, multi-view image compression is essential to various applications, including virtual reality and 3D modeling. Despite the considerable success of learning-based compression models on single images, limited progress has been made in multi-view image compression. In this paper, we propose an efficient approach to multi-view image compression by leveraging the redundant information across different viewpoints without explicitly using warping operations or camera parameters. Our method builds upon the recent advancements in Multi-Reference Entropy Models (MEM), which were initially proposed to capture correlations within an image. We extend the MEM models to employ cross-view correlations in addition to within-image correlations. Specifically, we generate latent representations for each view independently and integrate a cross-view context module within the entropy model. The estimation of entropy parameters for each view follows an autoregressive technique, leveraging correlations with the previous views. We show that adding this view context module further enhances the compression performance when jointly trained with the autoencoder. Experimental results demonstrate superior performance compared to both traditional and learning-based multi-view compression methods. 

Holographic displays are an upcoming technology for AR and VR applications, with the ability to show 3D content with accurate depth cues, including accommodation and motion parallax. Recent research reveals that only a fraction of holographic pixels are needed to display images with high fidelity, improving energy efficiency in future holographic displays. However, the existing iterative method for computing sparse amplitude and phase layouts does not run in real time; instead, it takes hundreds of milliseconds to render an image into a sparse hologram. In this paper, we present a non-iterative amplitude and phase computation for sparse Fourier holograms that uses Perlin noise in the image–plane phase. We conduct simulated and optical experiments. Compared to the Gaussian-weighted Gerchberg–Saxton method, our method achieves a run time improvement of over 600 times while producing a nearly equal PSNR and SSIM quality. The real-time performance of our method enables the presentation of dynamic content crucial to AR and VR applications, such as video streaming and interactive visualization, on holographic displays. 

Recently, several approaches have emerged for generating neural representations with multiple levels of detail (LODs). LODs can improve the rendering by using lower resolutions and smaller model sizes when appropriate. However, existing methods generally focus on a few discrete LODs which suffer from aliasing and flicker artifacts as details are changed and limit their granularity for adapting to resource limitations. In this paper, we propose a method to encode light field networks with continuous LODs, allowing for finely tuned adaptations to rendering conditions. Our training procedure uses summed-area table filtering allowing efficient and continuous filtering at various LODs. Furthermore, we use saliency-based importance sampling which enables our light field networks to distribute their capacity, particularly limited at lower LODs, towards representing the details viewers are most likely to focus on. Incorporating continuous LODs into neural representations enables progressive streaming of neural representations, decreasing the latency and resource utilization for rendering.