Publications de Kwang Moo Yi | EPFL Graph Search

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Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...

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 ...

In this paper, we develop an indoor localization device which embeds localization information into indoor light-emitting-diodes (LED) lighting systems. The key idea of our device is the use of the newly proposed "bit stuffing method". Through the use of st ...

Our demo demonstrates the method we published at CVPR this year for tracking specular and poorly textured objects, and lets the visitors experiment with it and with their own patterns. Our approach only requires a standard monocular camera (no need for a d ...

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by ﬁnding the eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix representing a linear sy ...

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 ...

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 ...

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 ...

The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the ...

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 ...

We present a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision. To do so we exploit depth and relative camera pose cues to create a virtual tar ...

Kwang Moo Yi

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Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

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

Development of a Localization System Based on VLC Technique for an Indoor Environment

[DEMO] Tracking Texture-less, Shiny Objects with Descriptor Fields

Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses

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

Image Matching Across Wide Baselines: From Paper to Practice

LIFT: Learned Invariant Feature Transform

The Visual Object Tracking VOT2014 Challenge Results

Learning to Find Good Correspondences

Authoring and Living Next-Generation Location-Based Experiences

LF-Net: Learning Local Features from Images

Visual tracking in complex scenes through pixel-wise tri-modeling (vol 26, pg 205, 2015)