Lightweight Binary Voxel Shape Features for 3D Data Matching and Retrieval
Lightweight Binary Voxel Shape Features for 3D Data Matching and Retrieval
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DOI：
10.1109/bigmm.2015.66
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发表时间：
2015-04
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影响因子：
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通讯作者：
Takahiro Matsuda;T. Furuya;Ryutarou Ohbuchi
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文献类型：
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作者：
Takahiro Matsuda;T. Furuya;Ryutarou Ohbuchi
本文针对三维体素数据提出了几种轻量级的局部三维形状特征，从而产生紧凑的二元特征向量。这些特征的灵感来自于二维图像的紧凑二值特征，即Local binary Pattern (LBP)[22]、BRIEF[6]和ORB[26]。除了紧凑之外，所提出的3D特征的提取成本也很低。此外，这些二元特征向量的比较非常有效，因为它们在汉明空间中的距离可以非常有效地计算出来。我们在基于形状的3D模型检索设置中对这些特征进行了实验评估，结果表明其中一些3D二进制特征与一些现有特征相比具有竞争力。根据基准数据库，建议的特征在某种程度上不如最先进的3D形状特征准确。然而，内存占用要紧凑得多，大约是非二进制3D形状特征的1/10，具有相当的检索精度。
This paper proposes several lightweight local 3D shape features for 3D voxel data that yield compact binary feature vectors. These features are inspired by compact binary features for 2D image, namely, Local Binary Pattern (LBP) [22], BRIEF [6] and ORB [26]. In addition to being compact, extraction of proposed 3D features is inexpensive. Furthermore, these binary feature vectors are very efficient to compare, as their distance in Hamming space can be computed very efficiently. Our experimental evaluation of these features in a shape-based 3D model retrieval setting showed that some of these 3D binary features perform competitively to some of existing features. Depending on benchmark database, proposed features are somewhat less accurate than or about as accurate as the state-of-the-art 3D shape features. However, memory footprint is much more compact, at about 1/10 of the non-binary 3D shape features having comparable retrieval accuracy.