260 citations · 308 across the 55 of their papers we have counts for
6 papers · 1 filter
Learning End-To-End Scene Flow by Distilling Single Tasks Knowledge
Filippo Aleotti, Matteo Poggi, Fabio Tosi +1
Scene flow is a challenging task aimed at jointly estimating the 3D structure and motion of the sensed environment. Although deep learning solutions achieve outstanding performance…
Real-Time Semantic Stereo Matching
Pier Luigi Dovesi, Matteo Poggi, Lorenzo Andraghetti +4
Scene understanding is paramount in robotics, self-navigation, augmented reality, and many other fields. To fully accomplish this task, an autonomous agent has to infer the 3D stru…
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…
Enhancing self-supervised monocular depth estimation with traditional visual odometry
Lorenzo Andraghetti, Panteleimon Myriokefalitakis, Pier Luigi Dovesi +4
Estimating depth from a single image represents an attractive alternative to more traditional approaches leveraging multiple cameras. In this field, deep learning yielded outstandi…
Guided Stereo Matching
Matteo Poggi, Davide Pallotti, Fabio Tosi +1
Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when…
Learning monocular depth estimation infusing traditional stereo knowledge
Fabio Tosi, Filippo Aleotti, Matteo Poggi +1
Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications. Recent works proved that this task could be learned without dire…