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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Automatic registration for 3D shapes using hybrid dimensionality-reduction shape descriptions
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Automatic registration for 3D shapes using hybrid dimensionality-reduction shape descriptions

机译：使用混合降维形状描述自动注册3D形状

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摘要

Automatic registration for 3D shapes is an attractive problem in computer vision. Various registration algorithms based on different surface representations have been developed for this topic. However, most of the existing algorithms suffer from some limitations mainly related to discriminating similarity metric, partially overlapping data, and the robustness to resolution, noise and occlusion. In this research, hybrid dimensionality-reduction shape descriptions (DRSD) are proposed for pair-wise registration, which aims to overcome these limitations. Based on recently emerging angle-preserving parameterization techniques such as Harmonic Maps and ABF, 3D shapes are described in low-dimension space with both local and global considerations. Therefore, searching for correspondences, verifying overlapping regions and calculating registration error all can be implemented in low-dimension space. Moreover, a large number of experiments, using both real and synthetic images, have been carried out to show the accuracy, efficiency and robustness of the hybrid DRSD algorithm.
机译：3D形状的自动配准是计算机视觉中的一个有吸引力的问题。针对该主题已经开发了基于不同表面表示的各种配准算法。但是，大多数现有算法都受到一些限制，这些限制主要与区分相似性度量，部分重叠的数据以及对分辨率，噪声和遮挡的鲁棒性有关。在这项研究中，提出了用于成对配准的混合降维形状描述（DRSD），旨在克服这些限制。基于最近出现的角度保持参数化技术（例如“谐波图”和ABF），在三维空间中描述了局部和全局考虑的3D形状。因此，可以在低维空间中实现搜索对应关系，验证重叠区域以及计算配准误差。此外，已经进行了使用真实图像和合成图像的大量实验，以显示混合DRSD算法的准确性，效率和鲁棒性。

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