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.
Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks.
We present a novel gradient-based multi-task learning (MTL) approach that balances training in multi-task systems by aligning the independent components of the training objective.
For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component.
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.
Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks.
In contrast to the previous art, we, for the first time, propose to estimate CTR with uncertainty bounds.
This paper addresses the challenge of localization of anatomical landmarks in knee X-ray images at different stages of osteoarthritis (OA).
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.
This paper addresses the challenge of dense pixel correspondence estimation between two images.
Ranked #2 on Dense Pixel Correspondence Estimation on HPatches
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.
The camera location for the query image is obtained via triangulation from two relative translation estimates using a RANSAC based approach.
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.
This paper presents a convolutional neural network based approach for estimating the relative pose between two cameras.