1 code implementation • 6 Oct 2022 • Andrea Conti, Matteo Poggi, Filippo Aleotti, Stefano Mattoccia
Depth perception is pivotal in many fields, such as robotics and autonomous driving, to name a few.
no code implementations • 4 Apr 2022 • Alessio Mingozzi, Andrea Conti, Filippo Aleotti, Matteo Poggi, Stefano Mattoccia
In this paper, we aim to address this task leveraging a single RGB frame without additional depth sensors.
1 code implementation • 28 Oct 2021 • Filippo Aleotti, Fabio Tosi, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
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 phones.
1 code implementation • ICCV 2021 • 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.
1 code implementation • CVPR 2021 • 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 videos.
1 code implementation • 2 Jan 2021 • Matteo Poggi, Seungryong Kim, Fabio Tosi, Sunok Kim, Filippo Aleotti, Dongbo Min, Kwanghoon Sohn, Stefano Mattoccia
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.
1 code implementation • ECCV 2020 • Filippo Aleotti, Fabio Tosi, Li Zhang, Matteo Poggi, Stefano Mattoccia
In many fields, self-supervised learning solutions are rapidly evolving and filling the gap with supervised approaches.
1 code implementation • ECCV 2020 • Matteo Poggi, Filippo Aleotti, Fabio Tosi, Giulio Zaccaroni, Stefano Mattoccia
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 such cue.
1 code implementation • 10 Jun 2020 • Filippo Aleotti, Giulio Zaccaroni, Luca Bartolomei, Matteo Poggi, Fabio Tosi, Stefano Mattoccia
Depth perception is paramount to tackle real-world problems, ranging from autonomous driving to consumer applications.
1 code implementation • CVPR 2020 • Matteo Poggi, Filippo Aleotti, Fabio Tosi, Stefano Mattoccia
Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all.
1 code implementation • CVPR 2020 • Fabio Tosi, Filippo Aleotti, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Luigi Di Stefano, Stefano Mattoccia
Whole understanding of the surroundings is paramount to autonomous systems.
1 code implementation • 22 Nov 2019 • Filippo Aleotti, Matteo Poggi, Fabio Tosi, Stefano Mattoccia
Scene flow is a challenging task aimed at jointly estimating the 3D structure and motion of the sensed environment.
1 code implementation • CVPR 2019 • Fabio Tosi, Filippo Aleotti, Matteo Poggi, Stefano Mattoccia
Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications.
Ranked #41 on Monocular Depth Estimation on KITTI Eigen split
4 code implementations • 29 Jun 2018 • Matteo Poggi, Filippo Aleotti, Fabio Tosi, Stefano Mattoccia
To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image.