The Pattern Analysis and Computer Vision (PAVIS) research line is actively developing an artificial intelligence (AI) infrastructure to understand our physical space and the human behaviors within it.
The focus is on creating intelligent systems that jointly accomplish two main goals:
Pattern Analysis and Computer Vision
Spatial AI
Perceive and understand autonomously the real physical world, both static and dynamic, with its corresponding spatial semantic (where are things, what is their function, which is the dynamic of the scene).
Social AI
Perceive and understand humans and their behaviors as observed by cameras in relation to the physical spaces they are interacting with.
Relevant Papers
2024
Bortolon M., Tsesmelis T., James S., Poiesi F., Del Bue A.
6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model
European Conference on Computer Vision
2024
Scarpellini G., Fiorini S., Giuliari F., Morerio P., Del Bue A.
DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly
IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024
2022
Giuliari F., Skenderi G., Cristani M., Wang Y., Del Bue A.
Spatial Commonsense Graph for Object Localisation in Partial Scenes
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2022-June, pp. 19496-19505
Principal Investigator
Pattern Analysis and Computer Vision
Videos
Intelligent Sensor Network for assistive AI Research
Introduction to MEMEX Project
Aworld - AI powered visual localization
PAVIS - Overview
SceneUnderLight - IIT - UNIVR - OSRAM
Drone Localization with Augmented Acoustic Imaging
IIT OpenTalk Magazine - Lab Highlights Show all >