91 citations · 107 across the 6 of their papers we have counts for
17 papers · 1 filter
SIMstack: A Generative Shape and Instance Model for Unordered Object Stacks
Zoe Landgraf, Raluca Scona, Tristan Laidlow +3
By estimating 3D shape and instances from a single view, we can capture information about an environment quickly, without the need for comprehensive scanning and multi-view fusion.…
In-Place Scene Labelling and Understanding with Implicit Scene Representation
Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger +1
Semantic labelling is highly correlated with geometry and radiance reconstruction, as scene entities with similar shape and appearance are more likely to come from similar classes.…
Deep Probabilistic Feature-metric Tracking
Binbin Xu, Andrew J. Davison, Stefan Leutenegger
Dense image alignment from RGB-D images remains a critical issue for real-world applications, especially under challenging lighting conditions and in a wide baseline setting. In th…
Bundle Adjustment on a Graph Processor
Joseph Ortiz, Mark Pupilli, Stefan Leutenegger +1
Graph processors such as Graphcore's Intelligence Processing Unit (IPU) are part of the major new wave of novel computer architecture for AI, and have a general design with massive…
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…
Towards Bounding-Box Free Panoptic Segmentation
Ujwal Bonde, Pablo F. Alcantarilla, Stefan Leutenegger
In this work we introduce a new Bounding-Box Free Network (BBFNet) for panoptic segmentation. Panoptic segmentation is an ideal problem for proposal-free methods as it already requ…