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Pascal Fua

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Publications associées (319)

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Automated Reconstruction of Tree Structures using Path Classifiers and Mixed Integer Programming

Pascal Fua, Engin Türetken, Fethallah Benmansour

Although tracing linear structures in 2D images and 3D image stacks has received much attention over the years, full automation remains elusive. In this paper, we formulate the delineation problem as one of solving a Quadratic Mixed Integer Program (Q-MIP) ...

2012

Multi-view Tracking UsingWeakly Supervised Human Motion Prediction

Pascal Fua, Weizhe Liu, Martin Pierre Engilberge

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections ...

IEEE COMPUTER SOC2023

Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

Pascal Fua, Artem Rozantsev

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a si ...

Ieee2017

Delineating Trees in Noisy 2D Images and 3D Image Stacks

Pascal Fua, François Fleuret, Engin Türetken, Germán González Serrano

We present a novel approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, our method builds a set of candidate trees over many different subsets of point ...

2010

Principled Detection-by-Classification from Multiple Views

Pascal Fua, François Fleuret

Machine-learning based classification techniques have been shown to be effective at detecting objects in complex scenes. However, the final results are often obtained from the alarms produced by the classifiers through a post-processing which typically rel ...

2008

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

Pascal Fua, Mathieu Salzmann, Mateusz Kozinski, Agata Justyna Mosinska

The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis. In this paper, we show that we can train a Deep Net to perform 3D volumetric delineation giv ...

2020

Retexturing in the Presence of Complex Illumination and Occlusions

Pascal Fua, Vincent Lepetit, Julien Pilet

We present a non-rigid registration technique that achieves spatial, photometric, and visibility accuracy. It lets us photo-realistically augment 3D deformable surfaces under complex illumination conditions and in spite of severe occlusions. There are many ...

2007

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

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

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

A fast local descriptor for dense matching

Pascal Fua, Vincent Lepetit, Engin Tola

We introduce a novel local image descriptor designed for dense wide-baseline matching purposes. We feed our descriptors to a graph-cuts based dense depth map estimation algorithm and this yields better wide-baseline performance than the commonly used corre ...

2008

Learning to Match Aerial Images with Deep Attentive Architectures

Pascal Fua, Eduard Trulls Fortuny

Image matching is a fundamental problem in Computer Vision. In the context of feature-based matching, SIFT and its variants have long excelled in a wide array of applications. However, for ultra-wide baselines, as in the case of aerial images captured unde ...

2016

Fast Object Detection with Entropy-Driven Evaluation

Pascal Fua, François Fleuret, Raphael Sznitman, Carlos Joaquin Becker

Cascade-style approaches to implementing ensemble classifiers can deliver significant speed-ups at test time. While highly effective, they remain challenging to tune and their overall performance depends on the availability of large validation sets to esti ...

2013

Do We Need Binary Features for 3D Reconstruction?

Pascal Fua, Xinchao Wang

Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors. They have been shown with promising results on some real time application ...

2016

Every Smile is Unique: Landmark-Guided Diverse Smile Generation

Pascal Fua, Wei Wang

Each smile is unique: one person surely smiles in different ways (e.g. closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video gen ...

IEEE2018

Human body pose detection using Bayesian spatio-temporal templates

Pascal Fua, Vincent Lepetit

We present a template-based approach to detecting human silhouettes in a specific walking pose. Our templates consist of short sequences of 2D silhouettes obtained from motion capture data. This lets us incorporate motion information into them and helps di ...

Elsevier2006

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

Efficient scanning for em based target localization

Pascal Fua, Graham Knott, Aurélien Lucchi, Raphael Sznitman

We presented an approach for speeding-up image acquisition when tasked with localizing specific structures in FIB-SEM imagery. It exploits the fact that low-quality images can be acquired faster than higher-quality ones and yet be sufficient for inference ...

2014

Efficient Large Scale Multi-View Stereo for Ultra High Resolution Image Sets

Pascal Fua, Christoph Strecha, Engin Tola

We present a new approach for large scale multi-view stereo matching, which is designed to operate on ultra high resolution image sets and efficiently compute dense 3D point clouds. We show that, by using a robust descriptor for matching purposes and high ...

Springer Verlag2012

Single-Stage 6D Object Pose Estimation

Pascal Fua, Mathieu Salzmann, Wei Wang, Yinlin Hu

Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm. This two-stage process, how ...

IEEE2020

GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping

Pascal Fua, Mathieu Salzmann, Victor Constantin, Erhan Gündogdu

While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inference in a deep network, requiring a single forward pass, is much faster. Taking ...

IEEE2019

Two-level Data Augmentation for Calibrated Multi-view Detection

Pascal Fua, Martin Pierre Engilberge

Data augmentation has proven its usefulness to improve model generalization and performance. While it is commonly applied in computer vision application when it comes to multi-view systems, it is rarely used. Indeed geometric data augmentation can break th ...

IEEE COMPUTER SOC2023