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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >An Efficient Global Point Cloud Descriptor for Object Recognition and Pose Estimation
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An Efficient Global Point Cloud Descriptor for Object Recognition and Pose Estimation

机译：用于对象识别和姿态估计的高效全局点云描述符

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摘要

This paper presents a global point cloud descriptor to be used for efficient object recognition and pose estimation. The proposed method is based on the estimation of a reference frame for the whole point cloud that represents an object instance, which is used for aligning it with the canonical coordinate system. After that, a descriptor is computed for the aligned point cloud based on how its 3D points are spatially distributed. Such descriptor is also extended with color distribution throughout the aligned point cloud. The global alignment transforms of matched point clouds are used for computing object pose. The proposed approach was evaluated with a publicly available dataset, showing that it outperforms major state of the art global descriptors regarding recognition rate and performance and that it allows precise pose estimation.
机译：本文提出了一种全局点云描述符，可用于有效的对象识别和姿态估计。所提出的方法基于代表对象实例的整个点云的参考框架的估计，该参考框架用于将其与规范坐标系对齐。此后，根据对齐的点云的3D点在空间上的分布方式，为它计算一个描述符。这样的描述符也随着颜色分布扩展到整个对齐的点云中。匹配点云的全局对齐变换用于计算对象姿态。用公开可用的数据集对提出的方法进行了评估，结果表明该方法在识别率和性能方面优于主要的全局描述符，并且可以进行精确的姿态估计。

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