160 citations · 780 across the 27 of their papers we have counts for
4 papers · 2 filters
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…
End-to-End Multi-Task Learning with Attention
Shikun Liu, Edward Johns, Andrew J. Davison
We propose a novel multi-task learning architecture, which allows learning of task-specific feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists o…