Search Results for author: Filippo Aleotti

Found 14 papers, 13 papers with code

Unsupervised confidence for LiDAR depth maps and applications

1 code implementation6 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.

Autonomous Driving

Monitoring social distancing with single image depth estimation

no code implementations4 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.

Depth Estimation

Neural Disparity Refinement for Arbitrary Resolution Stereo

1 code implementation28 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.

Zero-shot Generalization

Sensor-Guided Optical Flow

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.

Optical Flow Estimation

Learning optical flow from still images

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.

Monocular Depth Estimation Optical Flow Estimation

On the confidence of stereo matching in a deep-learning era: a quantitative evaluation

1 code implementation2 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.

Stereo Matching

Self-adapting confidence estimation for stereo

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.

Self-Supervised Learning

On the uncertainty of self-supervised monocular depth estimation

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.

Monocular Depth Estimation

Learning End-To-End Scene Flow by Distilling Single Tasks Knowledge

1 code implementation22 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.

Optical Flow Estimation

Towards real-time unsupervised monocular depth estimation on CPU

4 code implementations29 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.

Autonomous Navigation Image Reconstruction +2

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