Dense Non-rigid Shape Correspondence Using Random Forests
Dense Non-rigid Shape Correspondence Using Random Forests
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DOI：
10.1109/cvpr.2014.532
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
E. Rodolà;S. R. Bulò;Thomas Windheuser;Matthias Vestner;D. Cremers
中科院分区：
文献类型：
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作者：
E. Rodolà;S. R. Bulò;Thomas Windheuser;Matthias Vestner;D. Cremers
我们提出了一种形状匹配的方法，产生密集的对应调整到一个特定的形状和变形类。在一个场景中，这一类是由一个小的例子形状，所提出的方法学习的形状描述符捕捉给定类中的变形的变化。该方法使波核签名扩展类识别的变形从近等距的变形出现在一个随机森林分类器的装置的例子集。在引入空间正则化的帮助下，所提出的方法在基线方法上取得了显着的改进，并在保持较短计算时间的同时获得了最先进的结果。
We propose a shape matching method that produces dense correspondences tuned to a specific class of shapes and deformations. In a scenario where this class is represented by a small set of example shapes, the proposed method learns a shape descriptor capturing the variability of the deformations in the given class. The approach enables the wave kernel signature to extend the class of recognized deformations from near isometries to the deformations appearing in the example set by means of a random forest classifier. With the help of the introduced spatial regularization, the proposed method achieves significant improvements over the baseline approach and obtains state-of-the-art results while keeping short computation times.