SHREC'12 Track: Generic 3D Shape Retrieval
By Bo Li, A. Godil, Masaki Aono, X. Bai, Takahiko Furuya, L. Li, Roberto Javier López-Sastre, H. Johan, Ryutarou Ohbuchi, Carolina Redondo-Cabrera and 3 more. 2012
96 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Leonidas J. Guibas
- Matthias Nießner
- Hao Su
- Thomas A. Funkhouser
- Charles Ruizhongtai Qi
- Andreas Geiger
- Silvio Savarese
- Angela Dai
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Trevor Darrell
- Andrea Vedaldi
- Dacheng Tao
- Xiaogang Wang
- Ming-Hsuan Yang
- Abhinav Kumar Gupta
Citations in the corpus
- 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…”
- Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set (2015, background): “4 SHREC'12 [21] 2012 Mesh models Object retrieval 1,200/60 No http://www.”
- Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols (2015, unknown): “4 SHREC'12 [24] 2012 Mesh models Object retrieval 1200 / 60 No http://www.”
- A large-scale Shape Benchmark for 3D object retrieval: Toyohashi shape benchmark (2012, methodology): “4) Dense Voxel Spectrum Descriptor (DVD): Dense Voxel spectrum Descriptor (DVD) [19] is a shape descriptor accounting for the Fourier spectrum, based on the voxels and their block representation, defined by regularly spaced intervals and overlapping cells of blocks.”
- Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items (2022, methodology): “3D object datasets suitable for simulation research include those composed of 3D CAD models [41], [26], [42], [43], [44], [45], [11], [12], [13] and 3D scanned models [14], [46], [47], [48], [49], [15], [16], [50], [51] though many combine types [47].”
- Completing 3D object shape from one depth image (2015, methodology): “We introduce a synthetic dataset built from the SHREC12 mesh classification dataset [22].”