Three-dimensional shape searching: state-of-the-art review and future trends

By Natraj Iyer, Subramaniam Jayanti, K. Lou, Yagnanarayanan Kalyanaraman, Karthik Ramani. Comput. Aided Des., 2005

549 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7a2b670371a17cb91646beb038cbf9b4326429f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Zhangjie Cao
2. Karthik Ramani
3. Qi-Xing Huang
4. Fan Zhu
5. Yi Fang
6. J. Xie
7. Kunio Osada
8. Takahiko Furuya

Citations in the corpus

• Developing an engineering shape benchmark for CAD models (2006, background): “More details can also be found in [6,40].”
• A survey of content based 3D shape retrieval methods (2004, background): “[40] provides an extensive overview of 3D shape searching techniques.”
• Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, unknown): “The purpose of the analysis is then to find the dipole–dipole couplings and chemical shielding parameters that reproduce the experimental spectrum.”
• Deepshape: Deep learned shape descriptor for 3D shape matching and retrieval (2015, background): “Shape descriptors for shape matching and retrieval have been extensively studied in the geometry community [30, 14, 12, 31, 25].”
• Salient local visual features for shape-based 3D model retrieval (2008, background): “The need to organize these 3D models, for example for effective reuse, has prompted research into shape-based retrieval of 3D models [35] [15].”
• Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids (2006, methodology): “During the past forty years, a vast number of shape matching and searching techniques have been developed (e.g. using moments, spherical harmonics or Reeb graphs—a recent survey can be found in Iyer et al. [39], see also [30]).”
• 3D deep shape descriptor (2015, background): “The development of an effective and efficient 3D shape descriptor poses several technical challenges, including, in particular, the high data complexity of 3D models and their representations, the structural variations, noise, and incompleteness present in 3D models [47, 24, 15, 49, 43, 36, 27, 23, ”
• Intrinsic shape signatures: A shape descriptor for 3D object recognition (2009, background): “3D object representation and matching has been a fundamental part of computer vision research with applications in modeling, visualization, recognition, classification, and scene understanding [3][4][12].”
• 3D Object Classification via Spherical Projections (2017, background): “While early works focus on leveraging handcrafted features for classification [14, 1, 12, 8, 15, 9], recent methods seek to leverage the power of deep neural networks [27, 25].”
• SHREC 2007 3 D Shape Retrieval Contest (2006, unknown): “Athens, Greece [16] 1 P1 Shilane et al.”