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BAG-OF-FEATURES SAMPLING TECHNIQUES FOR 3D CAD MODEL RETRIEVAL

机译:用于3D CAD模型检索的袋式采样技术

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This paper investigates two sampling strategies, dense sampling and PHOW sampling, for bag-of-features 3D CAD model retrieval. Previous methods [1] use original salient SIFT feature detection for general 3D model retrieval which does not suit the need for CAD models representation. CAD models contain mostly piecewise-smooth surfaces and thus only sharp edges can be described. To overcome these limitations, two new sampling strategies are investigated to improve the feature extraction process. Dense sampling extracts SIFT features on regular spatial grids with even spacing. Pyramid Histogram Of visual Words (PHOW) [2] extracts features on repeatedly finer scales. Both the two sampling methods extract features that are covered the whole shape. In addition, the influences of codebook size and distance metric are also studied to optimize the retrieval performance. Experiments on Purdue Engineering Benchmark [3] show that the proposed sampling techniques achieve better retrieval accuracy than the original salient SIFT sampling method.
机译:本文调查了两种采样策略,密集采样和专用采样,适用于特点3D CAD模型检索。以前的方法[1]使用原始的突出SIFT特征检测,用于一般3D模型检索,不适合CAD模型表示的需要。 CAD模型主要包含分段光滑的表面,因此只有尖锐的边缘可以描述。为了克服这些限制,研究了两种新的抽样策略,以改善特征提取过程。致密采样在常规空间网格上提取筛选功能,甚至间距。视觉单词(手册)的金字塔直方图[2]在反复更精细的尺度上提取功能。两种采样方法都提取了整个形状的特征。此外,还研究了码本尺寸和距离度量的影响,以优化检索性能。 Purdue Engineering基准测试实验[3]表明,所提出的采样技术实现比原始突出的筛选采样方法更好的检索精度。

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