Search Results for author: Eduard Trulls

Found 14 papers, 10 papers with code

COTR: Correspondence Transformer for Matching Across Images

1 code implementation ICCV 2021 Wei Jiang, Eduard Trulls, Jan Hosang, Andrea Tagliasacchi, Kwang Moo Yi

We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other.

Dense Pixel Correspondence Estimation Optical Flow Estimation

DISK: Learning local features with policy gradient

1 code implementation NeurIPS 2020 Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls

Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints.

Ranked #3 on Image Matching on IMC PhotoTourism (using extra training data)

Image Matching reinforcement-learning

Image Matching across Wide Baselines: From Paper to Practice

5 code implementations3 Mar 2020 Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, Eduard Trulls

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.

Beyond Cartesian Representations for Local Descriptors

1 code implementation ICCV 2019 Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi, Pascal Fua, Eduard Trulls

We demonstrate that this representation is particularly amenable to learning descriptors with deep networks.

LF-Net: Learning Local Features from Images

3 code implementations NeurIPS 2018 Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi

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.

Learning to Find Good Correspondences

3 code implementations CVPR 2018 Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, Pascal Fua

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo.

Learning to Match Aerial Images With Deep Attentive Architectures

no code implementations CVPR 2016 Hani Altwaijry, Eduard Trulls, James Hays, Pascal Fua, Serge Belongie

We demonstrate that our models outperform the state-of-the-art on ultra-wide baseline matching and approach human accuracy.

LIFT: Learned Invariant Feature Transform

1 code implementation30 Mar 2016 Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, Pascal Fua

We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description.

Discriminative Learning of Deep Convolutional Feature Point Descriptors

1 code implementation ICCV 2015 Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, Francesc Moreno-Noguer

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e. g. SIFT.

Satellite Image Classification

Fracking Deep Convolutional Image Descriptors

no code implementations19 Dec 2014 Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Francesc Moreno-Noguer

In this paper we propose a novel framework for learning local image descriptors in a discriminative manner.

Segmentation-aware Deformable Part Models

no code implementations CVPR 2014 Eduard Trulls, Stavros Tsogkas, Iasonas Kokkinos, Alberto Sanfeliu, Francesc Moreno-Noguer

In this work we propose a technique to combine bottom-up segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs).

Optical Flow Estimation Superpixels

Dense Segmentation-Aware Descriptors

no code implementations CVPR 2013 Eduard Trulls, Iasonas Kokkinos, Alberto Sanfeliu, Francesc Moreno-Noguer

In this work we exploit segmentation to construct appearance descriptors that can robustly deal with occlusion and background changes.

Motion Estimation

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