260 citations · 327 across the 61 of their papers we have counts for
8 papers · 1 filter
Matching-space Stereo Networks for Cross-domain Generalization
Changjiang Cai, Matteo Poggi, Stefano Mattoccia +1
End-to-end deep networks represent the state of the art for stereo matching. While excelling on images framing environments similar to the training set, major drops in accuracy occ…
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…
Continual Adaptation for Deep Stereo
Matteo Poggi, Alessio Tonioni, Fabio Tosi +2
Depth estimation from stereo images is carried out with unmatched results by convolutional neural networks trained end-to-end to regress dense disparities. Like for most tasks, thi…
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…
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
Matteo Poggi, Fabio Tosi, Konstantinos Batsos +2
Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local,…
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…