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InpaintFusion: Incremental RGB-D Inpainting for 3D Scenes

机译:inpaintfusion:增量RGB-D批准3D场景

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State-of-the-art methods for diminished reality propagate pixel information from a keyframe to subsequent frames for real-time inpainting. However, these approaches produce artifacts, if the scene geometry is not sufficiently planar. In this article, we present InpaintFusion, a new real-time method that extends inpainting to non-planar scenes by considering both color and depth information in the inpainting process. We use an RGB-D sensor for simultaneous localization and mapping, in order to both track the camera and obtain a surfel map in addition to RGB images. We use the RGB-D information in a cost function for both the color and the geometric appearance to derive a global optimization for simultaneous inpainting of color and depth. The inpainted depth is merged in a global map by depth fusion. For the final rendering, we project the map model into image space, where we can use it for effects such as relighting and stereo rendering of otherwise hidden structures. We demonstrate the capabilities of our method by comparing it to inpainting results with methods using planar geometric proxies.
机译:用于减少现实的最先进的方法从关键帧传播像素信息,以进行实时染色的后续帧。然而,如果场景几何形状不够平坦,则这些方法产生伪影。在本文中,我们概述了一种新的实时方法,通过考虑在修复过程中的颜色和深度信息来延伸到非平面场景。我们使用RGB-D传感器进行同时定位和映射,以便还追踪摄像机并获得RGB图像的凸起贴图。我们使用RGB-D信息以成本函数用于颜色和几何外观,以导出全局优化,以便同时侵蚀颜色和深度。通过深度融合,染色深度在全球地图中合并。对于最终渲染,我们将地图模型投影到图像空间中,在那里我们可以使用它来实现效果,例如隐藏结构的致密和立体声渲染。我们通过使用平面几何代理将结果与方法进行比较来展示我们方法的能力。

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