160 citations · 628 across the 19 of their papers we have counts for
7 papers · 1 filter
Rearrangement: A Challenge for Embodied AI
Dhruv Batra, Angel X. Chang, Sonia Chernova +9
We describe a framework for research and evaluation in Embodied AI. Our proposal is based on a canonical task: Rearrangement. A standard task can focus the development of new techn…
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…
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion
Kentaro Wada, Edgar Sucar, Stephen James +2
Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion. Recognized preci…
NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction
Edgar Sucar, Kentaro Wada, Andrew Davison
The choice of scene representation is crucial in both the shape inference algorithms it requires and the smart applications it enables. We present efficient and optimisable multi-c…
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…