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The efficient method of mixed reality light restoration using HDR image of 3D scene

机译:使用HDR图像3D场景混合现实光恢复方法

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One of the main problems of MR devices is a physically correct representation of luminance distribution for virtual objects and its shadows in the real world. In other words, reconstruction of the luminance distribution over the scene is one of the key parameters that allow solving the problem of "seamless" interaction between the virtual and real worlds and serves as the primary means for improving the quality of perception when we use the augmented reality system. By using neuro networks, it becomes possible to estimate the number of light sources, their positions and radiation gonio - diagrams. Authors use the fast and high-quality photorealistic image synthesis to synthesize the virtual image of the real world under the restored illumination conditions. Having both real world image and synthesized real world image at restored illumination conditions it becomes possible to estimate the error of the roughly estimated light source positions to correct it. It is possible by application of the special shading analysis method. The first step requires two kind of information: reconstructed geometry of surrounding space and HDR image, with luminance data. One of the main issues is that there are two kinds of illumination: primary illumination, from light sources, and secondary illumination or background illumination, from non-luminous objects. The task is to remove background illumination and define primary illumination. After restoration illumination condition, a virtual object could be properly added to visible image of real environment.
机译:MR器件的主要问题之一是对虚拟对象的亮度分布的物理正确表示,并且在现实世界中的阴影。换句话说,在场景上重建亮度分布是允许解决虚拟和现实世界之间的“无缝”互动问题的关键参数之一,并用作提高当我们使用时的感知质量的主要方法增强现实系统。通过使用神经网络,可以估计光源,位置和辐射GONIO的数量。作者使用快速和高质量的光电态图像综合来在恢复的照明条件下综合现实世界的虚拟图像。在恢复的照明条件下具有真实世界的图像和合成的现实世界形象,可以估计粗略估计的光源位置的误差以校正它。通过应用特殊的阴影分析方法可以。第一步需要两种信息:具有亮度数据的周围空间和HDR图像的重建几何形状。主要问题之一是,来自非发光物体的光源和次级照明或背景照明有两种照明。任务是去除背景照明并定义主要照明。恢复照明条件后,可以将虚拟对象正确添加到真实环境的可见图像中。

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