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首页> 外文会议>IEEE International Conference on Image Processing >SUPERVOXEL-BASED SALIENCY DETECTION FOR LARGE-SCALE COLORED 3D POINT CLOUDS
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SUPERVOXEL-BASED SALIENCY DETECTION FOR LARGE-SCALE COLORED 3D POINT CLOUDS

机译：基于超级彩色3D点云的显着性检测

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摘要

Large-scale 3D point clouds have been actively used in many applications with the advent of capturing devices. In this paper, we propose a novel saliency detection algorithm for large-scale colored 3D point clouds which capture real-world scenes. We first voxelize an input point cloud, and then partition voxels into a supervoxel which corresponds to a clusters at the lowest level. We construct the super-voxel cluster hierarchy iteratively, where a high level cluster includes low level clusters which exhibit similar features to each other. We also estimate the saliency at each cluster by computing the distinctness of geometric and color features based on center-surround contrast. By averaging the multiscale saliency maps obtained at different levels of clusters, we obtain final saliency distribution. Experimental results demonstrate that the proposed algorithm extracts globally and locally salient regions from large-scale colored 3D point clouds faithfully by employing the geometric and photometric features together.
机译：在许多应用程序中，大规模3D点云已在许多应用程序中使用捕获设备的出现。在本文中，我们提出了一种新的显着性检测算法，用于捕获现实世界场景的大规模彩色3D点云。我们首先将输入点云体系化，然后将体素分隔到一个超级素，这对应于最低级别的簇。我们迭代地构建超级体级群集层次结构，其中高级簇包括彼此具有类似特征的低级集群。我们还通过计算基于中心环绕对比度的几何和颜色特征的明显性来估计每个集群的显着性。通过平均在不同级别的群集下获得的多尺度显着图，我们获得最终显着性分布。实验结果表明，通过使用几何和光度特征在一起，所提出的算法通过忠实地从大规模着色的3D点云提取全球和局部突出区域。

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