Search Results for author: Michael Bloesch

Found 15 papers, 3 papers with code

Simple Sensor Intentions for Exploration

no code implementations15 May 2020 Tim Hertweck, Martin Riedmiller, Michael Bloesch, Jost Tobias Springenberg, Noah Siegel, Markus Wulfmeier, Roland Hafner, Nicolas Heess

In particular, we show that a real robotic arm can learn to grasp and lift and solve a Ball-in-a-Cup task from scratch, when only raw sensor streams are used for both controller input and in the auxiliary reward definition.

Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM

no code implementations24 Feb 2020 Zoe Landgraf, Fabian Falck, Michael Bloesch, Stefan Leutenegger, Andrew Davison

Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels.

Learning Meshes for Dense Visual SLAM

no code implementations ICCV 2019 Michael Bloesch, Tristan Laidlow, Ronald Clark, Stefan Leutenegger, Andrew J. Davison

Estimating motion and surrounding geometry of a moving camera remains a challenging inference problem.

SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations

no code implementations CVPR 2019 Shuaifeng Zhi, Michael Bloesch, Stefan Leutenegger, Andrew J. Davison

Systems which incrementally create 3D semantic maps from image sequences must store and update representations of both geometry and semantic entities.

MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM

no code implementations19 Dec 2018 Binbin Xu, Wenbin Li, Dimos Tzoumanikas, Michael Bloesch, Andrew Davison, Stefan Leutenegger

It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene.

Instance Segmentation Object SLAM +2

Matching Features without Descriptors: Implicitly Matched Interest Points

1 code implementation26 Nov 2018 Titus Cieslewski, Michael Bloesch, Davide Scaramuzza

The extraction and matching of interest points is a prerequisite for many geometric computer vision problems.

Pose Estimation

Task-Embedded Control Networks for Few-Shot Imitation Learning

2 code implementations8 Oct 2018 Stephen James, Michael Bloesch, Andrew J. Davison

Despite this, most robot learning approaches have focused on learning a single task, from scratch, with a limited notion of generalisation, and no way of leveraging the knowledge to learn other tasks more efficiently.

Few-Shot Imitation Learning Imitation Learning +1

LS-Net: Learning to Solve Nonlinear Least Squares for Monocular Stereo

no code implementations ECCV 2018 Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison

In this paper, we propose LS-Net, a neural nonlinear least squares optimization algorithm which learns to effectively optimize these cost functions even in the presence of adversities.

Learning to Solve Nonlinear Least Squares for Monocular Stereo

no code implementations ECCV 2018 Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison

In this paper, we propose a neural nonlinear least squares optimization algorithm which learns to effectively optimize these cost functions even in the presence of adversities.

Fusion++: Volumetric Object-Level SLAM

no code implementations25 Aug 2018 John McCormac, Ronald Clark, Michael Bloesch, Andrew J. Davison, Stefan Leutenegger

Reconstructed objects are stored in an optimisable 6DoF pose graph which is our only persistent map representation.

Loop Closure Detection

CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM

no code implementations CVPR 2018 Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, Andrew J. Davison

Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency.

CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM

no code implementations3 Apr 2018 Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, Andrew J. Davison

Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency.

gvnn: Neural Network Library for Geometric Computer Vision

1 code implementation25 Jul 2016 Ankur Handa, Michael Bloesch, Viorica Patraucean, Simon Stent, John McCormac, Andrew Davison

We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning.

Image Reconstruction Visual Odometry

Cannot find the paper you are looking for? You can Submit a new open access paper.