Concept

Correspondence problem

Related publications (91)

A Performance Evaluation of Local Features for Image-Based 3D Reconstruction

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This paper performs a comprehensive and comparative evaluation of the state of the art local features for the task of image based 3D reconstruction. The evaluated local features cover the recently developed ones by using powerful machine learning technique ...

2019

Multiple Hypothesis Semantic Mapping for Robust Data Association

Roland Siegwart

In this letter, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomou ...

2019

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

Learning to Find Good Correspondences

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

IEEE2018

A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth and Optical Flow Estimation

Davide Scaramuzza

We present a unifying framework to solve several computer vision problems with event cameras: motion, depth and optical flow estimation. The main idea of our framework is to find the point trajectories on the image plane that are best aligned with the even ...

2018

Learning embeddings: efficient algorithms and applications

Cijo Jose

Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...

EPFL2018

Variational Methods for Human Modeling

Timur Bagautdinov

A large part of computer vision research is devoted to building models and algorithms aimed at understanding human appearance and behaviour from images and videos. Ultimately, we want to build automated systems that are at least as capable as people when i ...

EPFL2018

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

Pascal Fua, Mathieu Salzmann, Zheng Dang, Kwang Moo Yi, Fei Wang, Yinlin Hu

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 ...

2018

Learning embeddings: efficient algorithms and applications

Cijo Jose

École Polytechnique Fédérale de Lausanne2018

Data-Efficient Decentralized Visual SLAM

Davide Scaramuzza, Titus Cieslewski

Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for multi-robot applications in environments where absolute positioning is not available. Being visual, it relies on cheap, lightweight and versatile cameras, and, being d ...

2018

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