Publications by Leonardo Citraro

Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate

Pascal Fua, Doruk Oner, Mateusz Kozinski, Leonardo Citraro

Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data i ...

Institute of Electrical and Electronics Engineers2022

On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation

Pascal Fua, Sina Honari, Leonardo Citraro, Soumava Kumar Roy

Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. However, as the acquisition of ground-truth 3D labels is labor intensive and time consuming, recent attention has shifted towards semi- a ...

IEEE2022

Promoting Connectivity of Network-Like Structures by Enforcing Region Separation

Pascal Fua, Doruk Oner, Mateusz Kozinski, Leonardo Citraro

We propose a novel, connectivity-oriented loss function for training deep convolutional networks to reconstruct network-like structures, like roads and irrigation canals, from aerial images. The main idea behind our loss is to express the connectivity of r ...

2022

Real-time camera pose estimation for sports fields

Pascal Fua, Charles Dubout, Leonardo Citraro, Horesh Beny Ben Shitrit, Stefano Savarè, Pablo Marquez Neila

Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image ...

2020

Towards Reliable Evaluation of Algorithms for Road Network Reconstruction from Aerial Images

Pascal Fua, Mateusz Kozinski, Leonardo Citraro

Existing connectivity-oriented performance measures rank road delineation algorithms inconsistently, which makes it difficult to decide which one is best for a given application. We show that these inconsistencies stem from design flaws that make the metri ...

2020

TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation

Pascal Fua, Mateusz Kozinski, Leonardo Citraro, Subeesh Vasu

Most state-of-the-art approaches to road extraction from aerial images rely on a CNN trained to label road pixels as foreground and remainder of the image as background. The CNN is usually trained by minimizing pixel-wise losses, which is less than ideal t ...

2020