4 citations · 6 across the 5 of their papers we have counts for
7 papers
Generic Merging of Structure from Motion Maps with a Low Memory Footprint
Gabrielle Flood, David Gillsjö, Patrik Persson +2
With the development of cheap image sensors, the amount of available image data have increased enormously, and the possibility of using crowdsourced collection methods has emerged.…
Trust Your IMU: Consequences of Ignoring the IMU Drift
Marcus Valtonen Örnhag, Patrik Persson, Mårten Wadenbäck +2
In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us t…
Detailed 3D Human Body Reconstruction from Multi-view Images Combining Voxel Super-Resolution and Learned Implicit Representation
Zhongguo Li, Magnus Oskarsson, Anders Heyden
The task of reconstructing detailed 3D human body models from images is interesting but challenging in computer vision due to the high freedom of human bodies. In order to tackle t…
A novel joint points and silhouette-based method to estimate 3D human pose and shape
Zhongguo Li, Anders Heyden, Magnus Oskarsson
This paper presents a novel method for 3D human pose and shape estimation from images with sparse views, using joint points and silhouettes, based on a parametric model. Firstly, t…
Efficient Real-Time Radial Distortion Correction for UAVs
Marcus Valtonen Örnhag, Patrik Persson, Mårten Wadenbäck +2
In this paper we present a novel algorithm for onboard radial distortion correction for unmanned aerial vehicles (UAVs) equipped with an inertial measurement unit (IMU), that runs…
Minimal Solvers for Indoor UAV Positioning
Marcus Valtonen Örnhag, Patrik Persson, Mårten Wadenbäck +2
In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation. We focus on cases where additional informat…
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…