A 3D Shape Benchmark for Retrieval and Automatic Classification of Architectural Data

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ShapeNet: An Information-Rich 3D Model Repository (2015, methodology): “Source datasets from SHREC 2014: Princeton Shape Benchmark (PSB) [27], SHREC 2012 generic Shape Benchmark (SHREC12GTB) [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 B”
Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set (2015, background): “17 Bonn architecture benchmark [29] 2009 Mesh models Object retrieval 2,257 No ftp://ftp.”
A large-scale Shape Benchmark for 3D object retrieval: Toyohashi shape benchmark (2012, methodology): “ASB is suitable for evaluating architectural objects to simulate houses, buildings, and furniture including chairs and windows.”
Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols (2015, background): “17 Bonn Architecture Benchmark [31] 2009 Mesh models Object retrieval 2257 No ftp://ftp.”
Sketch-based 3D shape retrieval using Convolutional Neural Networks (2015, methodology): “There are some recent advancements for general and special objects, such as the SHREC’14 Benchmark [20] and the Bonn Architecture Benchmark [27].”