91 citations · 107 across the 9 of their papers we have counts for
6 papers · 1 filter
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
Binbin Xu, Wenbin Li, Dimos Tzoumanikas +3
We propose a new multi-instance dynamic RGB-D SLAM system using an object-level octree-based volumetric representation. It can provide robust camera tracking in dynamic environment…
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…
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
Wenbin Li, Sajad Saeedi, John McCormac +6
Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Loc…
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…
Towards an Embodied Semantic Fovea: Semantic 3D scene reconstruction from ego-centric eye-tracker videos
Mickey Li, Noyan Songur, Pavel Orlov +2
Incorporating the physical environment is essential for a complete understanding of human behavior in unconstrained every-day tasks. This is especially important in ego-centric tas…
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…