10 citations · 30 across the 4 of their papers we have counts for
5 papers · 1 filter
Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM
Zoe Landgraf, Fabian Falck, Michael Bloesch +2
Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels. The many approaches to labelli…
SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations
Shuaifeng Zhi, Michael Bloesch, Stefan Leutenegger +1
Systems which incrementally create 3D semantic maps from image sequences must store and update representations of both geometry and semantic entities. However, while there has been…
LS-Net: Learning to Solve Nonlinear Least Squares for Monocular Stereo
Ronald Clark, Michael Bloesch, Jan Czarnowski +2
Sum-of-squares objective functions are very popular in computer vision algorithms. However, these objective functions are not always easy to optimize. The underlying assumptions ma…
Fusion++: Volumetric Object-Level SLAM
John McCormac, Ronald Clark, Michael Bloesch +2
We propose an online object-level SLAM system which builds a persistent and accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered indoor…
CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM
Michael Bloesch, Jan Czarnowski, Ronald Clark +2
The representation of geometry in real-time 3D perception systems continues to be a critical research issue. Dense maps capture complete surface shape and can be augmented with sem…