Publications by Vincent Lepetit

Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions

Mathieu Salzmann, Vincent Lepetit, Yinlin Hu, Van Nguyen Nguyen, Yang Xiao

We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these objects nor real images depicting them, only their CAD models. It relies on a s ...

IEEE COMPUTER SOC2022

Learning to Find Good Correspondences

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny, Yuki Ono

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outlie ...

2018

Robust 3D Object Tracking from Monocular Images Using Stable Parts

Pascal Fua, Vincent Lepetit, Mahdi Rad, Alberto Crivellaro, Yannick Verdie, Kwang Moo Yi

2018

Learning to Find Good Correspondences

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny, Yuki Ono

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outlie ...

IEEE2018

Method, system, and device for learned invariant feature transform for computer images

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny

A method for training a feature detector of an image processing device, including the steps of detecting features in the image to generate a score map, computing a center of mass on the score map to generate a location, extracting a patch from the image at ...

2018

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

Detecting Flying Objects using a Single Moving Camera

Pascal Fua, Vincent Lepetit, Artem Rozantsev

We propose an approach for detecting flying objects such as Unmanned Aerial Vehicles (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. We a ...

2017

Learning Lightprobes for Mixed Reality Illumination

Pascal Fua, Vincent Lepetit, Kwang Moo Yi

This paper presents the first photometric registration pipeline for Mixed Reality based on high quality illumination estimation by convolutional neural network (CNN) methods. For easy adaptation and deployment of the system, we train the CNN using purely s ...

2017

Method, System and Device for Direct Prediction of 3D Body Poses from Motion Compensated Sequence

Pascal Fua, Vincent Lepetit, Bugra Tekin, Artem Rozantsev

A method for predicting three-dimensional body poses from image sequences of an object, the method performed on a processor of a computer having memory, the method including the steps of accessing the image sequences from the memory, finding bounding boxes ...

2017

LIFT: Learned Invariant Feature Transform

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny

We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description. While previous works have successfully tackled each one of these problems individu ...

Springer Int Publishing Ag2016

Structured Prediction of 3D Human Pose with Deep Neural Networks

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Bugra Tekin, Isinsu Katircioglu

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

2016

Direct Prediction of 3D Body Poses from Motion Compensated Sequences

Pascal Fua, Vincent Lepetit, Bugra Tekin, Artem Rozantsev

We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Previous approaches typically compute candidate poses in individual frames and then link them in a post-processi ...

2016

Learning to Assign Orientations to Feature Points

Pascal Fua, Vincent Lepetit, Yannick Verdie, Kwang Moo Yi

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction ...

2016

Vision-Based Unmanned Aerial Vehicle Detection and Tracking for Sense and Avoid Systems

Denis Gillet, Alcherio Martinoli, Pascal Fua, Vincent Lepetit, Steven Adriaan Roelofsen, Artem Rozantsev, Krishna Raj Sapkota

We propose an approach for on-line detection of small Unmanned Aerial Vehicles (UAVs) and estimation of their relative positions and velocities in the 3D environment from a single moving camera in the context of sense and avoid systems. This problem is cha ...

Ieee2016

Multiscale Centerline Detection

Pascal Fua, Vincent Lepetit, Engin Türetken, Amos Sironi

Finding the centerline and estimating the radius of linear structures is a critical first step in many applications, ranging from road delineation in 2D aerial images to modeling blood vessels, lung bronchi, and dendritic arbors in 3D biomedical image stac ...

Institute of Electrical and Electronics Engineers2016

On Rendering Synthetic Images for Training an Object Detector

Pascal Fua, Vincent Lepetit, Artem Rozantsev

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of ...

Elsevier2015

Learning Separable Filters

Pascal Fua, Vincent Lepetit, Roberto Rigamonti, Bugra Tekin, Amos Sironi

Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, mak ...

Ieee Computer Soc2015

Learning Image Descriptors with Boosting

Vincent Lepetit, Tomasz Piotr Trzcinski, Christos Marios Christoudias

We propose a novel and general framework to learn compact but highly discriminative floating-point and binary local feature descriptors. By leveraging the boosting-trick we first show how to efficiently train a compact floating-point descriptor that is ver ...

Institute of Electrical and Electronics Engineers2015

A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images

Pascal Fua, Vincent Lepetit, Mahdi Rad, Alberto Crivellaro, Yannick Verdie, Kwang Moo Yi

We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object. Our method relies only on grayscale images since depth cameras fail on metallic objects; it can handle poorly textured objects, and cluttered, c ...

2015

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