1 code implementation • 5 Feb 2017 • Iaroslav Melekhov, Juha Ylioinas, Juho Kannala, Esa Rahtu
This paper presents a convolutional neural network based approach for estimating the relative pose between two cameras.
no code implementations • 23 Mar 2017 • Iaroslav Melekhov, Juha Ylioinas, Juho Kannala, Esa Rahtu
In this paper, we propose an encoder-decoder convolutional neural network (CNN) architecture for estimating camera pose (orientation and location) from a single RGB-image.
no code implementations • 31 Jul 2017 • Zakaria Laskar, Iaroslav Melekhov, Surya Kalia, Juho Kannala
The camera location for the query image is obtained via triangulation from two relative translation estimates using a RANSAC based approach.
no code implementations • 31 Oct 2017 • Iaroslav Melekhov, Juho Kannala, Esa Rahtu
In this work we propose a neural network based image descriptor suitable for image patch matching, which is an important task in many computer vision applications.
4 code implementations • 19 Oct 2018 • Iaroslav Melekhov, Aleksei Tiulpin, Torsten Sattler, Marc Pollefeys, Esa Rahtu, Juho Kannala
This paper addresses the challenge of dense pixel correspondence estimation between two images.
Ranked #2 on Dense Pixel Correspondence Estimation on HPatches
Dense Pixel Correspondence Estimation Optical Flow Estimation +1
no code implementations • 15 Apr 2019 • Zakaria Laskar, Iaroslav Melekhov, Hamed R. -Tavakoli, Juha Ylioinas, Juho Kannala
The main contribution is a geometric correspondence verification approach for re-ranking a shortlist of retrieved database images based on their dense pair-wise matching with the query image at a pixel level.
4 code implementations • 29 Jul 2019 • Aleksei Tiulpin, Iaroslav Melekhov, Simo Saarakkala
This paper addresses the challenge of localization of anatomical landmarks in knee X-ray images at different stages of osteoarthritis (OA).
no code implementations • 8 Aug 2019 • Roman Solovyev, Iaroslav Melekhov, Timo Lesonen, Elias Vaattovaara, Osmo Tervonen, Aleksei Tiulpin
In contrast to the previous art, we, for the first time, propose to estimate CTR with uncertainty bounds.
no code implementations • 16 Aug 2020 • Iaroslav Melekhov, Gabriel J. Brostow, Juho Kannala, Daniyar Turmukhambetov
Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks.
no code implementations • 28 Oct 2020 • Jyri Maanpää, Josef Taher, Petri Manninen, Leo Pakola, Iaroslav Melekhov, Juha Hyyppä
Autonomous driving is challenging in adverse road and weather conditions in which there might not be lane lines, the road might be covered in snow and the visibility might be poor.
1 code implementation • ICCV 2021 • Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov, Xiaotian Li, Juho Kannala
For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component.
no code implementations • 10 Oct 2021 • Iaroslav Melekhov, Zakaria Laskar, Xiaotian Li, Shuzhe Wang, Juho Kannala
Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks.
no code implementations • 19 Dec 2022 • Jyri Maanpää, Iaroslav Melekhov, Josef Taher, Petri Manninen, Juha Hyyppä
Robustness of different pattern recognition methods is one of the key challenges in autonomous driving, especially when driving in the high variety of road environments and weather conditions, such as gravel roads and snowfall.
no code implementations • 5 May 2023 • Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov, Xiaotian Li, Yi Zhao, Giorgos Tolias, Juho Kannala
In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image.
1 code implementation • 26 Mar 2024 • Matias Turkulainen, Xuqian Ren, Iaroslav Melekhov, Otto Seiskari, Esa Rahtu, Juho Kannala
3D Gaussian splatting, a novel differentiable rendering technique, has achieved state-of-the-art novel view synthesis results with high rendering speeds and relatively low training times.
no code implementations • 16 Apr 2024 • Iaroslav Melekhov, Anand Umashankar, Hyeong-Jin Kim, Vladislav Serkov, Dusty Argyle
We introduce ECLAIR (Extended Classification of Lidar for AI Recognition), a new outdoor large-scale aerial LiDAR dataset designed specifically for advancing research in point cloud semantic segmentation.