33 citations · 118 across the 10 of their papers we have counts for
7 papers · 1 filter
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Sebastien Ehrhardt, Oliver Groth, Aron Monszpart +4
We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained…
Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar
Dan Barnes, Ingmar Posner
This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar. By embedding a differentiable poin…
Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information
Dan Barnes, Rob Weston, Ingmar Posner
This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor…
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning
Fabian B. Fuchs, Adam R. Kosiorek, Li Sun +2
The majority of contemporary object-tracking approaches do not model interactions between objects. This contrasts with the fact that objects' paths are not independent: a cyclist m…
Dropout Distillation for Efficiently Estimating Model Confidence
Corina Gurau, Alex Bewley, Ingmar Posner
We propose an efficient way to output better calibrated uncertainty scores from neural networks. The Distilled Dropout Network (DDN) makes standard (non-Bayesian) neural networks m…
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
Oliver Groth, Fabian B. Fuchs, Ingmar Posner +1
Physical intuition is pivotal for intelligent agents to perform complex tasks. In this paper we investigate the passive acquisition of an intuitive understanding of physical princi…