Publications de Vincent Lepetit

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We propose a real-time solution for modeling and tracking multiple 3D objects in unknown environments for Augmented Reality. The proposed solution consists of both scalable tracking and interactive modeling. Our contribution is twofold: First, we show how ...

The contribution of this paper is two-fold. First, we show how to extend the ESM algorithm to handle motion blur in 3D object track- ing. ESM is a powerful algorithm for template matching-based tracking, but it can fail under motion blur. We introduce an i ...

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 ...

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 ...

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 ...

We propose a robust method for registering high oblique images of landscapes. Typically, an input image can be registered by matching it against a set of registered images of the same location. While this has been shown to work very well for images of popu ...

We present a method that is able to track several 3D objects si- multaneously, robustly, and accurately in real-time. While many applications need to consider more than one object in practice, the existing methods for single object tracking do not scale we ...

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 ...

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 ...

We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive. We first identify good keypoint candidates in mu ...

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 ...

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 ...

Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods hav ...

This work collects the explorations conducted within the EPFL+ECAL Lab by several designers to interpret the various spheres of action of Augmented Reality in order to derive visual principles. These principles seek to contribute to developing a specific v ...

While there is a large class of Multiple-Target Tracking (MTT) problems for which batch processing is possible and desirable, batch MTT remains relatively unexplored in comparison to sequential approaches. In this paper, we give a principled probabilistic ...

We propose a robust and accurate method to extract the centerlines and scale of tubular structures in 2D images and 3D volumes. Existing techniques rely either on filters designed to respond to ideal cylindrical structures, which lose accuracy when the lin ...

In many 3--D object-detection and pose-estimation problems, run-time performance is of critical importance. However, there usually is time to train the system, which we will show to be very useful. Assuming that several registered images of the target obje ...

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 ...

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 ...

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 ...

Vincent Lepetit

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Real-time interactive modeling and scalable multiple object tracking for AR

ESM-Blur: Handling and Rendering Blur in 3D Tracking and Augmentation

A fast local descriptor for dense matching

Retexturing in the Presence of Complex Illumination and Occlusions

Human body pose detection using Bayesian spatio-temporal templates

Pose Estimation of Web-Shared Landscape Pictures

Multiple 3D Object Tracking for Augmented Reality

Learning to Assign Orientations to Feature Points

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

TILDE: A Temporally Invariant Learned DEtector

Learning Lightprobes for Mixed Reality Illumination

Multiscale Centerline Detection

Keypoint Signatures for Fast Learning and Recognition

Turning Augmented Reality into a media: Design exploration to build a dedicated visual language

General Constraints for Batch Multiple-Target Tracking Applied to Large-Scale Videomicroscopy

Multiscale Centerline Detection by Learning a Scale-Space Distance Transform

Keypoint Recognition using Randomized Trees

Learning Separable Filters

Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation

BRIEF: Binary Robust Independent Elementary Features