13 citations · 14 across the 7 of their papers we have counts for
14 papers · 1 filter
Unsupervised confidence for LiDAR depth maps and applications
Andrea Conti, Matteo Poggi, Filippo Aleotti +1
Depth perception is pivotal in many fields, such as robotics and autonomous driving, to name a few. Consequently, depth sensors such as LiDARs rapidly spread in many applications.…
Monitoring social distancing with single image depth estimation
Alessio Mingozzi, Andrea Conti, Filippo Aleotti +2
The recent pandemic emergency raised many challenges regarding the countermeasures aimed at containing the virus spread, and constraining the minimum distance between people result…
Neural Disparity Refinement for Arbitrary Resolution Stereo
Filippo Aleotti, Fabio Tosi, Pierluigi Zama Ramirez +4
We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile ph…
Sensor-Guided Optical Flow
Matteo Poggi, Filippo Aleotti, Stefano Mattoccia
This paper proposes a framework to guide an optical flow network with external cues to achieve superior accuracy either on known or unseen domains. Given the availability of sparse…
Learning optical flow from still images
Filippo Aleotti, Matteo Poggi, Stefano Mattoccia
This paper deals with the scarcity of data for training optical flow networks, highlighting the limitations of existing sources such as labeled synthetic datasets or unlabeled real…
On the confidence of stereo matching in a deep-learning era: a quantitative evaluation
Matteo Poggi, Seungryong Kim, Fabio Tosi +5
Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Along…
Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation
Filippo Aleotti, Fabio Tosi, Li Zhang +2
In many fields, self-supervised learning solutions are rapidly evolving and filling the gap with supervised approaches. This fact occurs for depth estimation based on either monocu…
Self-adapting confidence estimation for stereo
Matteo Poggi, Filippo Aleotti, Fabio Tosi +2
Estimating the confidence of disparity maps inferred by a stereo algorithm has become a very relevant task in the years, due to the increasing number of applications leveraging suc…
Real-time single image depth perception in the wild with handheld devices
Filippo Aleotti, Giulio Zaccaroni, Luca Bartolomei +3
Depth perception is paramount to tackle real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image represen…
On the uncertainty of self-supervised monocular depth estimation
Matteo Poggi, Filippo Aleotti, Fabio Tosi +1
Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all. Despite the astonishing results yielded by su…
Distilled Semantics for Comprehensive Scene Understanding from Videos
Fabio Tosi, Filippo Aleotti, Pierluigi Zama Ramirez +4
Whole understanding of the surroundings is paramount to autonomous systems. Recent works have shown that deep neural networks can learn geometry (depth) and motion (optical flow) f…
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…
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…
Towards real-time unsupervised monocular depth estimation on CPU
Matteo Poggi, Filippo Aleotti, Fabio Tosi +1
Unsupervised depth estimation from a single image is a very attractive technique with several implications in robotic, autonomous navigation, augmented reality and so on. This topi…