1 code implementation • 21 Mar 2024 • Matteo Bonotto, Luigi Sarrocco, Daniele Evangelista, Marco Imperoli, Alberto Pretto
When dealing with few-shot settings, i. e. with a small set of input views, the training could overfit those views, leading to artifacts and geometric and chromatic inconsistencies in the resulting rendering.
no code implementations • 7 Jun 2022 • Daniel Fusaro, Emilio Olivastri, Daniele Evangelista, Marco Imperoli, Emanuele Menegatti, Alberto Pretto
Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation.
1 code implementation • 2 Feb 2021 • Alessandro Saviolo, Matteo Bonotto, Daniele Evangelista, Marco Imperoli, Jacopo Lazzaro, Emanuele Menegatti, Alberto Pretto
The proposed approach achieves cutting-edge results without the need of training the models with real annotated data of human body parts.
no code implementations • 22 Mar 2016 • Marco Imperoli, Alberto Pretto
This paper introduces an active object detection and localization framework that combines a robust untextured object detection and 3D pose estimation algorithm with a novel next-best-view selection strategy.