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Pascal Fua

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Related publications (322)

Leveraging Self-Supervision for Cross-Domain Crowd Counting

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from ...

IEEE COMPUTER SOC2022

TOPO-Loss for continuity-preserving crack detection using deep learning

Pascal Fua, Radhakrishna Achanta, Katrin Beyer, Fernando Perez Cruz, Doruk Oner, Bryan German Pantoja Rosero

We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

2022

Counting People by Estimating People Flows

Pascal Fua, Mathieu Salzmann, Weizhe Liu

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness co ...

2022

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Patrick Moller Jensen, Mian Akbar Shah

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume ...

Springer2022

Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Jiancheng Yang, Rui Shi

The human annotations are imperfect, especially when produced by junior practitioners. Multi-expert consensus is usually regarded as golden standard, while this annotation protocol is too expensive to implement in many real-world projects. In this study, w ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

3D reconstruction of curvilinear structures with stereo matching deep convolutional neural networks

Pascal Fua, Cécile Hébert, Emad Oveisi, Okan Altingövde, Gulnaz Ganeeva, Anastasiia Mishchuk

Curvilinear structures frequently appear in microscopy imaging as the object of interest. Crystallographic defects, i.e dislocations, are one of the curvilinear structures that have been repeatedly investigated under transmission electron microscopy (TEM) ...

ELSEVIER2022

MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks

Pascal Fua, Benoît Alain René Guillard, Federico Stella

Unsigned Distance Fields (UDFs) can be used to represent non-watertight surfaces. However, current approaches to converting them into explicit meshes tend to either be expensive or to degrade the accuracy. Here, we extend the marching cube algorithm to han ...

Springer2022

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

Pascal Fua, Leonardo Citraro, Mateusz Kozinski, Doruk Oner

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, Soumava Kumar Roy, Leonardo Citraro, Sina Honari

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

Learning to Align Sequential Actions in the Wild

Pascal Fua, Bugra Tekin, Weizhe Liu

State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspon- dences across videos in time. They either learn frame-to- frame mapping across sequences, which does not leverage temporal information, or a ...

IEEE COMPUTER SOC2022

3D Pose Based Feedback For Physical Exercises

Pascal Fua, Mathieu Salzmann, Isinsu Katircioglu, Hugues François Rémi Vinzant, Sena Kiciroglu, Ziqi Zhao, Yuan Cheng, Ziyi Zhao

Unsupervised self-rehabilitation exercises and physical training can cause serious injuries if performed incorrectly. We introduce a learning-based framework that identifies the mistakes made by a user and proposes corrective measures for easier and safer ...

Springer2022

DIG: Draping Implicit Garment over the Human Body

Pascal Fua, Edoardo Remelli, Benoît Alain René Guillard, Ren Li

Existing data-driven methods for draping garments over human bodies, despite being effective, cannot handle garments of arbitrary topology and are typically not end-to-end differentiable. To address these limitations, we propose an end-to-end differentiabl ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Jiancheng Yang

2022

Long Term Motion Prediction Using Keyposes

Pascal Fua, Mathieu Salzmann, Sena Kiciroglu, Wei Wang

Long term human motion prediction is essential in safety-critical applications such as human-robot interaction and autonomous driving. In this paper we show that to achieve long term forecasting, predicting human pose at every time instant is unnecessary. ...

IEEE2022

Persistent Homology with Improved Locality Information for more Effective Delineation

Kathryn Hess Bellwald, Pascal Fua, Mateusz Kozinski, Adélie Eliane Garin, Doruk Oner

Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing methods are very global and ignore the location of topological features. In th ...

2022

Overcoming the Domain Gap in Neural Action Representations

Pascal Fua, Pavan P Ramdya, Semih Günel, Florian Aymanns, Sina Honari

Relating behavior to brain activity in animals is a fundamental goal in neuroscience, with practical applications in building robust brain-machine interfaces. However, the domain gap between individuals is a major issue that prevents the training of genera ...

SPRINGER2022

Promoting Connectivity of Network-Like Structures by Enforcing Region Separation

Pascal Fua, Leonardo Citraro, Mateusz Kozinski, Doruk Oner

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

HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation

Pascal Fua, Nicolas Talabot, Pierre Bruno Baqué, Subeesh Vasu, Artem Lukoianov

Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives are particularly good at. In this paper, we propose a representation combinin ...

2022

Self-supervised Segmentation via Background Inpainting

Pascal Fua, Mathieu Salzmann, Victor Constantin, Isinsu Katircioglu, Helge Jochen Rhodin

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively e ...

2022

Learning to Simulate Realistic LiDARs

Pascal Fua, Benoît Alain René Guillard

Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling. To alleviate this, we introduce a pipeline for data-driven simulation ...

2022

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