4 citations · 6 across the 5 of their papers we have counts for
7 papers · 1 filter
Learning Structure-from-Motion with Graph Attention Networks
Lucas Brynte, José Pedro Iglesias, Carl Olsson +1
In this paper we tackle the problem of learning Structure-from-Motion (SfM) through the use of graph attention networks. SfM is a classic computer vision problem that is solved tho…
On the Tightness of Semidefinite Relaxations for Rotation Estimation
Lucas Brynte, Viktor Larsson, José Pedro Iglesias +2
Why is it that semidefinite relaxations have been so successful in numerous applications in computer vision and robotics for solving non-convex optimization problems involving rota…
Monocular Depth Parameterizing Networks
Patrik Persson, Linn Öström, Carl Olsson
Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these are able to use recognition of image features to predict r…
Accurate Optimization of Weighted Nuclear Norm for Non-Rigid Structure from Motion
José Pedro Iglesias, Carl Olsson, Marcus Valtonen Örnhag
Fitting a matrix of a given rank to data in a least squares sense can be done very effectively using 2nd order methods such as Levenberg-Marquardt by explicitly optimizing over a b…
Bilinear Parameterization For Differentiable Rank-Regularization
Marcus Valtonen Örnhag, Carl Olsson, Anders Heyden
Low rank approximation is a commonly occurring problem in many computer vision and machine learning applications. There are two common ways of optimizing the resulting models. Eith…
Rotation Averaging and Strong Duality
Anders Eriksson, Carl Olsson, Fredrik Kahl +1
In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its convent…