Imperial College London
Alternate

ProfessorAndrewDavison

Faculty of Engineering, Department of Computing

Professor of Robot Vision
 
 
 
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Contact

 

+44 (0)20 7594 8316a.davison Website

 
 
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Assistant

 

Ms Lucy Atthis +44 (0)20 7594 8259

 
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Location

 

303William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Kong:2023:10.1109/CVPR52729.2023.00098,
author = {Kong, X and Liu, S and Taher, M and Davison, AJ},
doi = {10.1109/CVPR52729.2023.00098},
pages = {952--961},
title = {vMAP: Vectorised Object Mapping for Neural Field SLAM},
url = {http://dx.doi.org/10.1109/CVPR52729.2023.00098},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We present vMAP, an object-level dense SLAM system using neural field representations. Each object is repre-sented by a small MLP, enabling efficient, watertight object modelling without the needfor 3D priors. As an RGB-D camera browses a scene with no prior in-formation, vMAP detects object instances on-the-fly, and dynamically adds them to its map. Specifically, thanks to the power of vectorised training, vMAP can optimise as many as 50 individual objects in a single scene, with an extremely efficient training speed of 5Hz map update. We experimentally demonstrate significantly improved scene-level and object-level reconstruction quality compared to prior neural field SLAM systems. Project page: https://kxhit.github.io/vMAP.
AU  - Kong,X
AU  - Liu,S
AU  - Taher,M
AU  - Davison,AJ
DO  - 10.1109/CVPR52729.2023.00098
EP  - 961
PY  - 2023///
SN  - 1063-6919
SP  - 952
TI  - vMAP: Vectorised Object Mapping for Neural Field SLAM
UR  - http://dx.doi.org/10.1109/CVPR52729.2023.00098
ER  -
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