三维局部表面描述符在低分辨率和高分辨率距离图像配准中的性能评价

S. A. A. Shah, Bennamoun, F. Boussaïd
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引用次数: 12

摘要

尽管低成本商业传感器(如微软Kinect)的出现和普及,3D视觉的研究仍然主要集中在面向高分辨率数据的先进算法的开发上。本文介绍了著名的最先进的三维局部表面描述符在高分辨率和低分辨率范围图像数据配准任务中的比较性能评估。这些实验中使用的数据集是著名的高分辨率斯坦福3D模型数据集和具有挑战性的低分辨率华盛顿RGB-D对象数据集。实验结果表明,局部表面描述符的性能受到低分辨率数据的显著影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation of 3D Local Surface Descriptors for Low and High Resolution Range Image Registration
Despite the advent and popularity of low-cost commercial sensors (e.g., Microsoft Kinect), research in 3D vision still primarily focuses on the development of advanced algorithms geared towards high resolution data. This paper presents a comparative performance evaluation of renowned state-of-the-art 3D local surface descriptors for the task of registration of both high and low resolution range image data. The datasets used in these experiments are the renowned high resolution Stanford 3D models dataset and challenging low resolution Washington RGB-D object dataset. Experimental results show that the performance of certain local surface descriptors is significantly affected by low resolution data.
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