Local Shape Descriptors, a Survey and Evaluation
Local Shape Descriptors, a Survey and Evaluation
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DOI：
10.2312/3dor/3dor11/049-056
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发表时间：
2011-04
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影响因子：
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通讯作者：
Paul M. Heider;Alain Pierre-Pierre-Alain-Pierre-Pierre-1438300103;Ruosi Li;C. Grimm
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作者：
Paul M. Heider;Alain Pierre-Pierre-Alain-Pierre-Pierre-1438300103;Ruosi Li;C. Grimm
本地形状描述符可用于各种任务，从注册到比较再到形状分析和检索。已经为这些任务开发了各种局部形状描述符，这些局部形状描述符已经单独或成对地进行了评估，但没有相互比较。我们提供了对现有描述符的调查以及用于比较它们的框架。我们使用来自各种来源的真实数据集对描述符进行详细评估。我们首先评估这些度量在网格分辨率、噪声和平滑变化下的稳定性。然后，我们分析了描述子对形状匹配任务的区分能力。我们的结论是，对正态分布和平均曲率进行采样，使用25个样本，并通过主成分分析将这些数据减少到5-10个样本，可以提供对噪声的稳健性和最佳的形状识别结果。
Local shape descriptors can be used for a variety of tasks, from registration to comparison to shape analysis and retrieval. There have been a variety of local shape descriptors developed for these tasks, which have been evaluated in isolation or in pairs, but not against each other. We provide a survey of existing descriptors and a framework for comparing them. We perform a detailed evaluation of the descriptors using real data sets from a variety of sources. We first evaluate how stable these metrics are under changes in mesh resolution, noise, and smoothing. We then analyze the discriminatory ability of the descriptors for the task of shape matching. Our conclusion is that sampling the normal distribution and the mean curvature, using 25 samples, and reducing this data to 5-10 samples via Principal Components Analysis provides robustness to noise and the best shape discrimination results.