Search Results for author: Andrew Davison

Found 13 papers, 4 papers with code

Asymptotics of $\ell_2$ Regularized Network Embeddings

1 code implementation5 Jan 2022 Andrew Davison

A common approach to solving prediction tasks on large networks, such as node classification or link prediction, begin by learning a Euclidean embedding of the nodes of the network, from which traditional machine learning methods can then be applied.

Link Prediction Node Classification

Asymptotics of Network Embeddings Learned via Subsampling

2 code implementations6 Jul 2021 Andrew Davison, Morgane Austern

We prove, under the assumption that the graph is exchangeable, that the distribution of the learned embedding vectors asymptotically decouples.

Link Prediction Node Classification +1

Attention-driven Robotic Manipulation

no code implementations1 Jan 2021 Stephen James, Andrew Davison

Despite the success of reinforcement learning methods, they have yet to have their breakthrough moment when applied to a broad range of robotic manipulation tasks.

reinforcement-learning

Ego-Centric Spatial Memory Networks

no code implementations ICLR 2021 Daniel James Lenton, Stephen James, Ronald Clark, Andrew Davison

With our broad demonstrations, we show that ESMN represents a useful and general computation graph for embodied spatial reasoning, and the module forms a bridge between real-time mapping systems and differentiable memory architectures.

Inductive Bias Semantic Segmentation

Next Waves in Veridical Network Embedding

no code implementations10 Jul 2020 Owen G. Ward, Zhen Huang, Andrew Davison, Tian Zheng

Embedding nodes of a large network into a metric (e. g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences.

Community Detection Link Prediction +2

NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction

no code implementations9 Apr 2020 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.

3D Object Reconstruction

Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM

no code implementations24 Feb 2020 Zoe Landgraf, Fabian Falck, Michael Bloesch, Stefan Leutenegger, Andrew Davison

Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels.

Event-based Vision: A Survey

4 code implementations17 Apr 2019 Guillermo Gallego, Tobi Delbruck, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew Davison, Joerg Conradt, Kostas Daniilidis, Davide Scaramuzza

Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur.

Event-based vision

MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM

no code implementations19 Dec 2018 Binbin Xu, Wenbin Li, Dimos Tzoumanikas, Michael Bloesch, Andrew Davison, Stefan Leutenegger

It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene.

Instance Segmentation Object SLAM +2

Semantic Texture for Robust Dense Tracking

no code implementations29 Aug 2017 Jan Czarnowski, Stefan Leutenegger, Andrew Davison

We argue that robust dense SLAM systems can make valuable use of the layers of features coming from a standard CNN as a pyramid of `semantic texture' which is suitable for dense alignment while being much more robust to nuisance factors such as lighting than raw RGB values.

SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks

no code implementations16 Sep 2016 John McCormac, Ankur Handa, Andrew Davison, Stefan Leutenegger

This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions.

gvnn: Neural Network Library for Geometric Computer Vision

1 code implementation25 Jul 2016 Ankur Handa, Michael Bloesch, Viorica Patraucean, Simon Stent, John McCormac, Andrew Davison

We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning.

Image Reconstruction Visual Odometry

Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM

no code implementations18 May 2015 Michael Milford, Hanme Kim, Michael Mangan, Stefan Leutenegger, Tom Stone, Barbara Webb, Andrew Davison

Event-based cameras offer much potential to the fields of robotics and computer vision, in part due to their large dynamic range and extremely high "frame rates".

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