no code implementations • 4 Dec 2023 • Weiwei Sun, Eduard Trulls, Yang-Che Tseng, Sneha Sambandam, Gopal Sharma, Andrea Tagliasacchi, Kwang Moo Yi
We overcome these problems with a simple representation that aggregates point clouds at multiple scale levels with sparse voxel grids at different resolutions.
1 code implementation • NeurIPS 2023 • Paul-Edouard Sarlin, Eduard Trulls, Marc Pollefeys, Jan Hosang, Simon Lynen
Semantic 2D maps are commonly used by humans and machines for navigation purposes, whether it's walking or driving.
no code implementations • ICCV 2023 • Michał J. Tyszkiewicz, Pascal Fua, Eduard Trulls
Diffusion models generating images conditionally on text, such as Dall-E 2 and Stable Diffusion, have recently made a splash far beyond the computer vision community.
no code implementations • 16 Jun 2022 • Yuhe Jin, Weiwei Sun, Jan Hosang, Eduard Trulls, Kwang Moo Yi
Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e. g. elbow, digit, abstract geometric shape) appears only once in an image.
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.
Ranked #1 on Dense Pixel Correspondence Estimation on KITTI 2012
Dense Pixel Correspondence Estimation Optical Flow Estimation
3 code implementations • 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 #2 on Image Matching on IMC PhotoTourism (using extra training data)
5 code implementations • 3 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.
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.
1 code implementation • CVPR 2020 • Weiwei Sun, Wei Jiang, Eduard Trulls, Andrea Tagliasacchi, Kwang Moo Yi
Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds.
1 code implementation • ICCV 2019 • Wei Jiang, Weiwei Sun, Andrea Tagliasacchi, Eduard Trulls, Kwang Moo Yi
We propose a novel image sampling method for differentiable image transformation in deep neural networks.
4 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.
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.
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.
1 code implementation • 30 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.
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.
Ranked #2 on Satellite Image Classification on SAT-4
no code implementations • 19 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.
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).
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.