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Pascal Fua

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Publications associées (319)

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Accurate Non-Iterative O(n) Solution to the PnP Problem

Pascal Fua, Vincent Lepetit

We propose a non-iterative solution to the PnP problem---the estimation of the pose of a calibrated camera from n 3D-to-2D point correspondences---whose computational complexity grows linearly with n. This is in contrast to state-of-the-art method that are ...

2007

Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation

Pascal Fua, Vincent Lepetit, Julien Pilet

We present a real-time method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge. Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be d ...

2008

BRIEF: Binary Robust Independent Elementary Features

Pascal Fua, Vincent Lepetit, Christoph Strecha, Michael Calonder

We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF. We show that it is highly discriminative even when using relatively few bits and can be computed using simple intensity difference tests. Furthermore, the descr ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2010

Local deformation models for monocular 3D shape recovery

Pascal Fua, Mathieu Salzmann, Raquel Urtasun

Without a deformation model, monocular 3D shape recovery of deformable surfaces is severly under-constrained. Even when the image information is rich enough, prior knowledge of the feasible deformations is required to overcome the ambiguities. This is furt ...

2008

Learning Latent Representations of 3D Human Pose with Deep Neural Networks

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Bugra Tekin

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from an image to a 3D pose, which ignores the dependencies between human joints, or model these dependencies ...

2018

Efficient anatomical labeling of pulmonary tree structures via deep point-graph representation-based implicit fields

Kangxian Xie, Pascal Fua, Jiancheng Yang

Pulmonary diseases rank prominently among the principal causes of death worldwide. Curing them will require, among other things, a better understanding of the complex 3D tree-shaped structures within the pulmonary system, such as airways, arteries, and vei ...

2025

Image Matching Across Wide Baselines: From Paper to Practice

Pascal Fua, Kwang Moo Yi, Eduard Trulls Fortuny, Anastasiia Mishchuk

We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task-the accuracy of the reconstructed camera pose-as our primary metric. Our pipeline's modular structure allows easy integration, confi ...

2021

Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming

Pascal Fua, Engin Türetken, Fethallah Benmansour, Przemyslaw Rafal Glowacki

We propose a novel Bayesian approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discrimina ...

2016

Geometrically-Conditioned Point Diffusion Models

Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz

Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision com- munity. Here, we tackle the related problem of generating point clouds, both unconditi ...

2023

Making Background Subtraction Robust to Sudden Illumination Changes

Pascal Fua, Christoph Strecha, Julien Pilet

Modern background subtraction techniques can handle gradual illumination changes but can easily be confused by rapid ones. We propose a technique that overcomes this limitation by relying on a statistical model, not of the pixel intensities, but of the ill ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

Template-Free Monocular Reconstruction of Deformable Surfaces

Pascal Fua, Mathieu Salzmann, Engin Tola, Aydin Varol

It has recently been shown that deformable 3D surfaces could be recovered from single video streams. However, existing techniques either require a reference view in which the shape of the surface is known a priori, which often may not be available, or requ ...

2009

Real-Time Seamless Single Shot 6D Object Pose Prediction

Pascal Fua, Bugra Tekin

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [ ...

2018

IA Generative Model for True Orthorectification

Pascal Fua, Christoph Strecha

Orthographic images compose an efficient and economic way to represent aerial images. This kind of information allows to measure two-dimensional objects and relate these to Geographic Information Systems. This paper deals with the computation of a true ort ...

2008

Flying Objects Detection from a Single Moving Camera

Pascal Fua, Vincent Lepetit, Artem Rozantsev

We propose an approach to detect flying objects such as UAVs and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. Solving such a difficult problem re ...

2015

Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Pascal Fua, Mathieu Salzmann, Weizhe Liu, Krzysztof Maciej Lis

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density in the image plane. While useful for this purpose, this image- plane density has no immediate physical meaning because it is subject to perspecti ...

IEEE2019

On the Relevance of Sparsity for Image Classification

Pascal Fua, Vincent Lepetit, Engin Türetken, Germán González Serrano, Roberto Rigamonti, Matthew Brown, Fethallah Benmansour

In this paper we empirically analyze the importance of sparsifying representations for classification purposes. We focus on those obtained by convolving images with linear filters, which can be either hand designed or learned, and perform extensive experim ...

Elsevier2014

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

Pascal Fua, Pamuditha Udaranga Wickramasinghe, 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

Real-time camera pose estimation for sports fields

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

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

Data-Driven Visual Tracking in Retinal Microsurgery

Pascal Fua, Raphael Sznitman, Karim Ali

In the context of retinal microsurgery, visual tracking of instruments is a key component of robotics assistance. The difficulty of the task and major reason why most existing strategies fail on {\it in-vivo} image sequences lies in the fact that complex a ...

2012

Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík

We propose a method for the unsupervised reconstruction of a temporally-coherent sequence of surfaces from a sequence of time-evolving point clouds, yielding dense, semantically meaningful correspondences between all keyframes. We represent the reconstruct ...

IEEE2021