1,143 research outputs found

    Efficient Hybrid Image Warping for High Frame-Rate Stereoscopic Rendering

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    Modern virtual reality simulations require a constant high-frame rate from the rendering engine. They may also require very low latency and stereo images. Previous rendering engines for virtual reality applications have exploited spatial and temporal coherence by using image-warping to re-use previous frames or to render a stereo pair at lower cost than running the full render pipeline twice. However these previous approaches have shown artifacts or have not scaled well with image size. We present a new image-warping algorithm that has several novel contributions: an adaptive grid generation algorithm for proxy geometry for image warping; a low-pass hole-filling algorithm to address un-occlusion; and support for transparent surfaces by efficiently ray casting transparent fragments stored in per-pixel linked lists of an A-Buffer. We evaluate our algorithm with a variety of challenging test cases. The results show that it achieves better quality image-warping than state-of-the-art techniques and that it can support transparent surfaces effectively. Finally, we show that our algorithm can achieve image warping at rates suitable for practical use in a variety of applications on modern virtual reality equipment

    RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos

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    Obtaining the ground truth labels from a video is challenging since the manual annotation of pixel-wise flow labels is prohibitively expensive and laborious. Besides, existing approaches try to adapt the trained model on synthetic datasets to authentic videos, which inevitably suffers from domain discrepancy and hinders the performance for real-world applications. To solve these problems, we propose RealFlow, an Expectation-Maximization based framework that can create large-scale optical flow datasets directly from any unlabeled realistic videos. Specifically, we first estimate optical flow between a pair of video frames, and then synthesize a new image from this pair based on the predicted flow. Thus the new image pairs and their corresponding flows can be regarded as a new training set. Besides, we design a Realistic Image Pair Rendering (RIPR) module that adopts softmax splatting and bi-directional hole filling techniques to alleviate the artifacts of the image synthesis. In the E-step, RIPR renders new images to create a large quantity of training data. In the M-step, we utilize the generated training data to train an optical flow network, which can be used to estimate optical flows in the next E-step. During the iterative learning steps, the capability of the flow network is gradually improved, so is the accuracy of the flow, as well as the quality of the synthesized dataset. Experimental results show that RealFlow outperforms previous dataset generation methods by a considerably large margin. Moreover, based on the generated dataset, our approach achieves state-of-the-art performance on two standard benchmarks compared with both supervised and unsupervised optical flow methods. Our code and dataset are available at https://github.com/megvii-research/RealFlowComment: ECCV 2022 Ora

    Proxy-guided Image-based Rendering for Mobile Devices

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    VR headsets and hand-held devices are not powerful enough to render complex scenes in real-time. A server can take on the rendering task, but network latency prohibits a good user experience. We present a new image-based rendering (IBR) architecture for masking the latency. It runs in real-time even on very weak mobile devices, supports modern game engine graphics, and maintains high visual quality even for large view displacements. We propose a novel server-side dual-view representation that leverages an optimally-placed extra view and depth peeling to provide the client with coverage for filling disocclusion holes. This representation is directly rendered in a novel wide-angle projection with favorable directional parameterization. A new client-side IBR algorithm uses a pre-transmitted level-of-detail proxy with an encaging simplification and depth-carving to maintain highly complex geometric detail. We demonstrate our approach with typical VR / mobile gaming applications running on mobile hardware. Our technique compares favorably to competing approaches according to perceptual and numerical comparisons

    A robust patch-based synthesis framework for combining inconsistent images

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    Current methods for combining different images produce visible artifacts when the sources have very different textures and structures, come from far view points, or capture dynamic scenes with motions. In this thesis, we propose a patch-based synthesis algorithm to plausibly combine different images that have color, texture, structural, and geometric inconsistencies. For some applications such as cloning and stitching where a gradual blend is required, we present a new method for synthesizing a transition region between two source images, such that inconsistent properties change gradually from one source to the other. We call this process image melding. For gradual blending, we generalized patch-based optimization foundation with three key generalizations: First, we enrich the patch search space with additional geometric and photometric transformations. Second, we integrate image gradients into the patch representation and replace the usual color averaging with a screened Poisson equation solver. Third, we propose a new energy based on mixed L2/L0 norms for colors and gradients that produces a gradual transition between sources without sacrificing texture sharpness. Together, all three generalizations enable patch-based solutions to a broad class of image melding problems involving inconsistent sources: object cloning, stitching challenging panoramas, hole filling from multiple photos, and image harmonization. We also demonstrate another application which requires us to address inconsistencies across the images: high dynamic range (HDR) reconstruction using sequential exposures. In this application, the results will suffer from objectionable artifacts for dynamic scenes if the inconsistencies caused by significant scene motions are not handled properly. In this thesis, we propose a new approach to HDR reconstruction that uses information in all exposures while being more robust to motion than previous techniques. Our algorithm is based on a novel patch-based energy-minimization formulation that integrates alignment and reconstruction in a joint optimization through an equation we call the HDR image synthesis equation. This allows us to produce an HDR result that is aligned to one of the exposures yet contains information from all of them. These two applications (image melding and high dynamic range reconstruction) show that patch based methods like the one proposed in this dissertation can address inconsistent images and could open the door to many new image editing applications in the future

    Depth-based Multi-View 3D Video Coding

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    INTERMEDIATE VIEW RECONSTRUCTION FOR MULTISCOPIC 3D DISPLAY

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    This thesis focuses on Intermediate View Reconstruction (IVR) which generates additional images from the available stereo images. The main application of IVR is to generate the content of multiscopic 3D displays, and it can be applied to generate different viewpoints to Free-viewpoint TV (FTV). Although IVR is considered a good approach to generate additional images, there are some problems with the reconstruction process, such as detecting and handling the occlusion areas, preserving the discontinuity at edges, and reducing image artifices through formation of the texture of the intermediate image. The occlusion area is defined as the visibility of such an area in one image and its disappearance in the other one. Solving IVR problems is considered a significant challenge for researchers. In this thesis, several novel algorithms have been specifically designed to solve IVR challenges by employing them in a highly robust intermediate view reconstruction algorithm. Computer simulation and experimental results confirm the importance of occluded areas in IVR. Therefore, we propose a novel occlusion detection algorithm and another novel algorithm to Inpaint those areas. Then, these proposed algorithms are employed in a novel occlusion-aware intermediate view reconstruction that finds an intermediate image with a given disparity between two input images. This novelty is addressed by adding occlusion awareness to the reconstruction algorithm and proposing three quality improvement techniques to reduce image artifices: filling the re-sampling holes, removing ghost contours, and handling the disocclusion area. We compared the proposed algorithms to the previously well-known algorithms on each field qualitatively and quantitatively. The obtained results show that our algorithms are superior to the previous well-known algorithms. The performance of the proposed reconstruction algorithm is tested under 13 real images and 13 synthetic images. Moreover, analysis of a human-trial experiment conducted with 21 participants confirmed that the reconstructed images from our proposed algorithm have very high quality compared with the reconstructed images from the other existing algorithms
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