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首页> 外文期刊>IEEE transactions on visualization and computer graphics >Superpixels of RGB-D Images for Indoor Scenes Based on Weighted Geodesic Driven Metric
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Superpixels of RGB-D Images for Indoor Scenes Based on Weighted Geodesic Driven Metric

机译：基于测地线驱动度量的室内场景RGB-D图像超像素

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摘要

Serving as a key step for applications of image processing, superpixel generation has been attracting increasing attention. RGB-D images are used pervasively in scenes reconstruction and representation, benefiting from their contained depth data. In this paper, we present a novel framework for generating superpixels focus on RGB-D images of indoor scenes, based on a weighted geodesic driven metric that combines both color and geometric information. In particular, taking into account the unique structures of indoor scenarios, we first denoise the given RGB-D image, and construct the corresponding triangular mesh. A new weighted geodesic driven metric is defined by introducing a weight function constrained with normal vectors and colors. Under this metric, an energy function is defined to measure our over-segmentation of the triangular mesh, by optimizing which, we can acquire an optimal over-segmentation of the triangular mesh with object boundaries respected, such that vertices in each sub-region have similar geometric structures and color intensities. Re-mapping the over-segmentation of the triangular mesh to the RGB-D image results in desired superpixels. We perform extensive experiments on a large-scale database of RGB-D images to verify the efficacy of our algorithm. The results show that our algorithm has considerable advantages over the existing state-of-the-art methods.
机译：作为图像处理应用的关键步骤，超像素生成已引起越来越多的关注。受益于其所包含的深度数据，RGB-D图像已广泛用于场景重建和表示中。在本文中，我们基于结合了颜色和几何信息的加权测地驱动指标，提出了一种新颖的框架，用于生成针对室内场景的RGB-D图像的超像素。特别是，考虑到室内场景的独特结构，我们首先对给定的RGB-D图像进行去噪，然后构建相应的三角形网格。通过引入受法向矢量和颜色约束的权重函数，可以定义新的加权测地线驱动度量。在此度量标准下，定义了一个能量函数以测量三角形网格的过度分割，通过优化该函数，我们可以在考虑对象边界的情况下获得三角形网格的最佳过度分割，以使每个子区域中的顶点具有相似的几何结构和颜色强度。将三角形网格的过度分割重新映射到RGB-D图像会产生所需的超像素。我们在RGB-D图像的大型数据库上进行了广泛的实验，以验证算法的有效性。结果表明，与现有技术相比，我们的算法具有明显的优势。

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