Developing an engineering shape benchmark for CAD models

By Subramaniam Jayanti, Yagnanarayanan Kalyanaraman, Natraj Iyer, Karthik Ramani. 2006

313 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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In Computational Mechanics

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Citations in the corpus

ShapeNet: An Information-Rich 3D Model Repository (2015, background): “…[16], Toyohashi Shape Benchmark (TSB) [29], Konstanz 3D Model Benchmark (CCCC) [32], Watertight Model Benchmark (WMB) [31], McGill 3D Shape Benchmark (MSB) [37], Bonn Architecture Benchmark (BAB) [33], Purdue Engineering Shape Benchmark (ESB) [9]. each shape and correspondences between shapes.”
The evolution, challenges, and future of knowledge representation in product design systems (2013, background): “[201] with the motivation of determining whether various shape representations have enough shape content in them to discriminate between different forms that exist in the engineering domain.”
A large-scale Shape Benchmark for 3D object retrieval: Toyohashi shape benchmark (2012, methodology): “Engineering Shape Benchmark (ESB) [5] is a 3D shape benchmark primarily for mechanical parts.”
SHREC 2007 3 D Shape Retrieval Contest (2006, background): “Generality represents the fraction of relevant items to a given query with respect to the irrelevant embedding, i.e. the whole set of non-relevant models in the database; as observed in [2], it is a major parameter in influencing the retrieval performance.”
SHREC'14 Track: Large Scale Comprehensive 3D Shape Retrieval (2014, methodology): “…the Toyohashi Shape Benchmark ( TSB ) [TKA12], the Konstanz 3D Model Benchmark ( CCCC ) [Vra04], the Watertight Model Benchmark ( WMB ) [VtH07], the McGill 3D Shape Benchmark ( MSB ) [SZM ∗ 08], the Bonn Architecture Benchmark ( BAB ) [WBK09], and the Engineering Shape Benchmark ( ESB ) [JKIR06].”
3-D Object Retrieval and Recognition With Hypergraph Analysis (2012, background): “Therefore, efficient 3-D object retrieval and recognition technologies [3]–[6] are of great importance for many applications, including computeraided design (CAD) [7] and molecular biology [8].”
Deep Learning Advances in Computer Vision with 3D Data (2017, methodology): “Experiments on two popular datasets, that is, Princeton Shape Benchmark (PSB) [Shilane et al. 2004] and Engineering Shape Benchmark (ESB) [Jayanti et al. 2006], indicated that the proposed architecture performed better compared to other global descriptors-based methods (e.g., LFD [Chen et al.…”