16 citations · 39 across the 32 of their papers we have counts for
6 papers · 1 filter
Unsupervised Domain Adaptation for Depth Prediction from Images
Alessio Tonioni, Matteo Poggi, Stefano Mattoccia +1
State-of-the-art approaches to infer dense depth measurements from images rely on CNNs trained end-to-end on a vast amount of data. However, these approaches suffer a drastic drop…
Semi-Automatic Labeling for Deep Learning in Robotics
Daniele De Gregorio, Alessio Tonioni, Gianluca Palli +1
In this paper, we propose Augmented Reality Semi-automatic labeling (ARS), a semi-automatic method which leverages on moving a 2D camera by means of a robot, proving precise camera…
Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC
Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz +5
Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption c…
Learning to Adapt for Stereo
Alessio Tonioni, Oscar Rahnama, Thomas Joy +3
Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment. Even though deep learning based stereo methods are succe…
Learning Across Tasks and Domains
Pierluigi Zama Ramirez, Alessio Tonioni, Samuele Salti +1
Recent works have proven that many relevant visual tasks are closely related one to another. Yet, this connection is seldom deployed in practice due to the lack of practical method…
Domain invariant hierarchical embedding for grocery products recognition
Alessio Tonioni, Luigi Di Stefano
Recognizing packaged grocery products based solely on appearance is still an open issue for modern computer vision systems due to peculiar challenges. Firstly, the number of differ…