44 citations · 100 across the 6 of their papers we have counts for
4 papers · 1 filter
Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes
Binbin Xu, Andrew J. Davison, Stefan Leutenegger
In this paper, we propose a novel object-level mapping system that can simultaneously segment, track, and reconstruct objects in dynamic scenes. It can further predict and complete…
ILabel: Interactive Neural Scene Labelling
Shuaifeng Zhi, Edgar Sucar, Andre Mouton +3
Joint representation of geometry, colour and semantics using a 3D neural field enables accurate dense labelling from ultra-sparse interactions as a user reconstructs a scene in rea…
SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks
John McCormac, Ankur Handa, Andrew Davison +1
Ever more robust, accurate and detailed mapping using visual sensing has proven to be an enabling factor for mobile robots across a wide variety of applications. For the next level…
gvnn: Neural Network Library for Geometric Computer Vision
Ankur Handa, Michael Bloesch, Viorica Patraucean +3
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of S…