VLDB 2026 Research / reviewers in the wild / expert
Fabrizio Natola
dblp:176/1552
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
2 papers |
Geometric modeling and processing · 58% Rendering · 42% | |
| Artificial intelligence
1 paper |
Video understanding and tracking · 44% Probabilistic and Bayesian machine learning · 44% Representation and self-supervised learning · 13% |
Topics — the 11 heaviest of 12, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering
bidirectional reflectance distribution function |
0.2 | 1 | 2016 | Single Image Object Modeling Based on BRDF and r-Surfaces Learning · CVPR 2016 |
Rendering
reflectance modeling |
0.2 | 1 | 2016 | Single Image Object Modeling Based on BRDF and r-Surfaces Learning · CVPR 2016 |
Geometric modeling and processing
surface reconstruction |
0.2 | 1 | 2016 | Single Image Object Modeling Based on BRDF and r-Surfaces Learning · CVPR 2016 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
bayesian nonparametric model |
0.2 | 1 | 2015 | Bayesian Non-parametric Inference for Manifold Based MoCap Representation · ICCV 2015 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian nonparametric model
dirichlet process mixture model |
0.2 | 1 | 2015 | Bayesian Non-parametric Inference for Manifold Based MoCap Representation · ICCV 2015 |
Computer vision › Video understanding and tracking › action recognition
human action recognition |
0.2 | 1 | 2015 | Bayesian Non-parametric Inference for Manifold Based MoCap Representation · ICCV 2015 |
Computer vision › Video understanding and tracking › motion analysis
motion capture analysis |
0.2 | 1 | 2015 | Bayesian Non-parametric Inference for Manifold Based MoCap Representation · ICCV 2015 |
Geometric modeling and processing › shape modeling › 3d object modeling
articulated object modeling |
0.2 | 1 | 2015 | Component-Wise Modeling of Articulated Objects · ICCV 2015 |
Geometric modeling and processing
shape deformation |
0.2 | 1 | 2015 | Component-Wise Modeling of Articulated Objects · ICCV 2015 |
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
manifold learning |
0.1 | 1 | 2015 | Bayesian Non-parametric Inference for Manifold Based MoCap Representation · ICCV 2015 |
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction › manifold learning
principal geodesic analysis |
0.1 | 1 | 2015 | Bayesian Non-parametric Inference for Manifold Based MoCap Representation · ICCV 2015 |
Methods — techniques the papers use, named apart from their topics
photo-consistency · 0.2normal field transfer · 0.2principal geodesic analysis · 0.2optimization · 0.2dirichlet process mixture · 0.2contour fitting · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Single Image Object Modeling Based on BRDF and r-Surfaces LearningabstractA methodology for 3D surface modeling from a single image is proposed. The principal novelty is concave and specular surface modeling without any externally imposed prior. The main idea of the method is to use BRDFs and generated rendered surfaces, to transfer the normal field, computed for the generated samples, to the unknown surface. The transferred information is adequate to blow and sculpt the segmented image mask in to a bas-relief of the object. The object surface is further refined basing on a photo-consistency formulation that relates for error minimization the original image and the modeled object. Fabrizio Natola, Valsamis Ntouskos, Fiora Pirri, Marta Sanzari |
CVPR | 1 |
| 2015 | Bayesian Non-parametric Inference for Manifold Based MoCap RepresentationabstractWe propose a novel approach to human action recognition, with motion capture data (MoCap), based on grouping sub-body parts. By representing configurations of actions as manifolds, joint positions are mapped on a subspace via principal geodesic analysis. The reduced space is still highly informative and allows for classification based on a non-parametric Bayesian approach, generating behaviors for each sub-body part. Having partitioned the set of joints, poses relative to a sub-body part are exchangeable, given a specified prior and can elicit, in principle, infinite behaviors. The generation of these behaviors is specified by a Dirichlet process mixture. We show with several experiments that the recognition gives very promising results, outperforming methods requiring temporal alignment. Fabrizio Natola, Valsamis Ntouskos, Marta Sanzari, Fiora Pirri |
ICCV | 1 |
| 2015 | Component-Wise Modeling of Articulated ObjectsabstractWe introduce a novel framework for modeling articulated objects based on the aspects of their components. By decomposing the object into components, we divide the problem in smaller modeling tasks. After obtaining 3D models for each component aspect by employing a shape deformation paradigm, we merge them together, forming the object components. The final model is obtained by assembling the components using an optimization scheme which fits the respective 3D models to the corresponding apparent contours in a reference pose. The results suggest that our approach can produce realistic 3D models of articulated objects in reasonable time. Valsamis Ntouskos, Marta Sanzari, Bruno Cafaro, Fabrizio Natola, Fiora Pirri, Manuel A. Ruiz Garcia |
ICCV | 5 |