Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment

By Silvia Biasotti, Andrea Cerri, A. Bronstein, Michael M. Bronstein. 2016

144 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geometry and Topology

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

1. Michael M. Bronstein
2. Or Litany
3. Andreas Geiger
4. Michael Niemeyer
5. Emanuele Rodolà
6. A. Bronstein
7. Joan Bruna
8. Lars M. Mescheder

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Andreas Geiger
3. Jitendra Malik
4. Dacheng Tao
5. Ming-Hsuan Yang
6. Xiaogang Wang
7. Andrea Vedaldi
8. Trevor Darrell

In Geometry and Topology

1. Michael M. Bronstein
2. Emanuele Rodolà
3. Or Litany
4. Ameesh Makadia
5. A. Bronstein
6. Pierre Vandergheynst
7. Joan Bruna
8. Michael Niemeyer

Citations in the corpus

• Geometric Deep Learning: Going beyond Euclidean data (2016, background): “[IN7] 3D shape correspondence application: Finding intrinsic correspondence between deformable shapes is a classical tough problem that underlies a broad range of vision and graphics applications, including texture mapping, animation, editing, and scene understanding [107].”
• Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017, background): “Since a detailed review of the literature would be out of scope for this paper, we refer the interested reader to the recent surveys on shape correspondence [39, 6].”
• Deformable Shape Completion with Graph Convolutional Autoencoders (2017, methodology): “on-rigid shape correspondence [23,12,32,45,9] is a fundamental challenge as it is an enabler for many high level tasks like pose or texture transfer across surfaces. We refer the interested reader to [54,4] for a detailed review of the literature. The proposed method in this work builds upon corresp”
• Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics (2019, background): “Due to limited scope, we restrict the discussion to only the most relevant works, referring the reader to [6, 60, 66] for a thorough discussion.”
• Image Matching from Handcrafted to Deep Features: A Survey (2020, methodology): “We refer the readers to the recent surveys (Biasotti et al. 2016; Van Kaick et al. 2011) in the shape matching area given that a detailed review of the literature is beyond the scope for this paper.”