Emanuele Rodola'
| Titolo | Pubblicato in | Anno |
|---|---|---|
| Product manifold filter: Non-rigid shape correspondence via kernel density estimation in the product space | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 | 2017 |
| Computing and processing correspondences with functional maps | ACM SIGGRAPH 2017 Courses, SIGGRAPH 2017 | 2017 |
| Deep functional maps: structured prediction for dense shape correspondence | Proceedings of the IEEE International Conference on Computer Vision | 2017 |
| Fully spectral partial shape matching | COMPUTER GRAPHICS FORUM | 2017 |
| SHREC'17: deformable shape retrieval with missing parts | Eurographics Workshop on 3D Object Retrieval | 2017 |
| Efficient globally optimal 2D-to-3D deformable shape matching | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | 2016 |
| SHREC'16: matching of deformable shapes with topological noise | Eurographics Workshop on 3D Object Retrieval, EG 3DOR | 2016 |
| SHREC'16: partial matching of deformable shapes | Eurographics Workshop on 3D Object Retrieval, EG 3DOR | 2016 |
| Coupled functional maps | Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016 | 2016 |
| Shape analysis with anisotropic windowed Fourier transform | Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016 | 2016 |
| Matching deformable objects in clutter | Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016 | 2016 |
| Learning shape correspondence with anisotropic convolutional neural networks | Advances in Neural Information Processing Systems | 2016 |
| Computing and processing correspondences with functional maps | SA 2016 - SIGGRAPH ASIA 2016 Courses | 2016 |
| Geometric deep learning | SA 2016 - SIGGRAPH ASIA 2016 Courses | 2016 |
| Anisotropic diffusion descriptors | COMPUTER GRAPHICS FORUM | 2016 |
| An accurate and robust artificial marker based on cyclic codes | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2016 |
| Non-Rigid Puzzles | COMPUTER GRAPHICS FORUM | 2016 |
| Deep learning for shape analysis | EG '16 Proceedings of the 37th Annual Conference of the European Association for Computer Graphics: Tutorials | 2016 |
| Applying random forests to the problem of dense non-rigid shape correspondence | Perspectives in Shape Analysis | 2016 |
| A simple and effective relevance-based point sampling for 3D shapes | PATTERN RECOGNITION LETTERS | 2015 |
Geometric and graph deep learning, computer vision, geometry processing, applied artificial intelligence, machine learning
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