Browsing by Author "Miandji, Ehsan"

Now showing 1 - 4 of 4
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    Compression and Real-Time Rendering of Inward Looking Spherical Light Fields
    (The Eurographics Association, 2020) Hajisharif, Saghi; Miandji, Ehsan; Baravadish, Gabriel; Larsson, Per; Unger, Jonas; Wilkie, Alexander and Banterle, Francesco
    Photorealistic rendering is an essential tool for immersive virtual reality. In this regard, the data structure of choice is typically light fields since they contain multidimensional information about the captured environment that can provide motion parallax and view-dependent information such as highlights. There are various ways to acquire light fields depending on the nature of the scene, limitations on the capturing setup, and the application at hand. Our focus in this paper is on full-parallax imaging of large-scale static objects for photorealistic real-time rendering. To this end, we introduce and simulate a new design for capturing inward-looking spherical light fields, and propose a system for efficient compression and real-time rendering of such data using consumer-level hardware suitable for virtual reality applications.
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    Light Field Video Compression and Real Time Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Hajisharif, Saghi; Miandji, Ehsan; Larsson, Per; Tran, Kiet; Unger, Jonas; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Light field imaging is rapidly becoming an established method for generating flexible image based description of scene appearances. Compared to classical 2D imaging techniques, the angular information included in light fields enables effects such as post-capture refocusing and the exploration of the scene from different vantage points. In this paper, we describe a novel GPU pipeline for compression and real-time rendering of light field videos with full parallax. To achieve this, we employ a dictionary learning approach and train an ensemble of dictionaries capable of efficiently representing light field video data using highly sparse coefficient sets. A novel, key element in our representation is that we simultaneously compress both image data (pixel colors) and the auxiliary information (depth, disparity, or optical flow) required for view interpolation. During playback, the coefficients are streamed to the GPU where the light field and the auxiliary information are reconstructed using the dictionary ensemble and view interpolation is performed. In order to realize the pipeline we present several technical contributions including a denoising scheme enhancing the sparsity in the dataset which enables higher compression ratios, and a novel pruning strategy which reduces the size of the dictionary ensemble and leads to significant reductions in computational complexity during the encoding of a light field. Our approach is independent of the light field parameterization and can be used with data from any light field video capture system. To demonstrate the usefulness of our pipeline, we utilize various publicly available light field video datasets and discuss the medical application of documenting heart surgery.
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    Single Sensor Compressive Light Field Video Camera
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Hajisharif, Saghi; Miandji, Ehsan; Guillemot, Christine; Unger, Jonas; Panozzo, Daniele and Assarsson, Ulf
    This paper presents a novel compressed sensing (CS) algorithm and camera design for light field video capture using a single sensor consumer camera module. Unlike microlens light field cameras which sacrifice spatial resolution to obtain angular information, our CS approach is designed for capturing light field videos with high angular, spatial, and temporal resolution. The compressive measurements required by CS are obtained using a random color-coded mask placed between the sensor and aperture planes. The convolution of the incoming light rays from different angles with the mask results in a single image on the sensor; hence, achieving a significant reduction on the required bandwidth for capturing light field videos. We propose to change the random pattern on the spectral mask between each consecutive frame in a video sequence and extracting spatioangular- spectral-temporal 6D patches. Our CS reconstruction algorithm for light field videos recovers each frame while taking into account the neighboring frames to achieve significantly higher reconstruction quality with reduced temporal incoherencies, as compared with previous methods. Moreover, a thorough analysis of various sensing models for compressive light field video acquisition is conducted to highlight the advantages of our method. The results show a clear advantage of our method for monochrome sensors, as well as sensors with color filter arrays.
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    SparseBTF: Sparse Representation Learning for Bidirectional Texture Functions
    (The Eurographics Association, 2023) Kavoosighafi, Behnaz; Frisvad, Jeppe Revall; Hajisharif, Saghi; Unger, Jonas; Miandji, Ehsan; Ritschel, Tobias; Weidlich, Andrea
    We propose a novel dictionary-based representation learning model for Bidirectional Texture Functions (BTFs) aiming at compact storage, real-time rendering performance, and high image quality. Our model is trained once, using a small training set, and then used to obtain a sparse tensor containing the model parameters. Our technique exploits redundancies in the data across all dimensions simultaneously, as opposed to existing methods that use only angular information and ignore correlations in the spatial domain. We show that our model admits efficient angular interpolation directly in the model space, rather than the BTF space, leading to a notably higher rendering speed than in previous work. Additionally, the high quality-storage cost tradeoff enabled by our method facilitates controlling the image quality, storage cost, and rendering speed using a single parameter, the number of coefficients. Previous methods rely on a fixed number of latent variables for training and testing, hence limiting the potential for achieving a favorable quality-storage cost tradeoff and scalability. Our experimental results demonstrate that our method outperforms existing methods both quantitatively and qualitatively, as well as achieving a higher compression ratio and rendering speed.