2.3k citations · 3.6k across the 50 of their papers we have counts for
58 papers
vMAP: Vectorised Object Mapping for Neural Field SLAM
Xin Kong, Shikun Liu, Marwan Taher +1
We present vMAP, an object-level dense SLAM system using neural field representations. Each object is represented by a small MLP, enabling efficient, watertight object modelling wi…
Learning a Depth Covariance Function
Eric Dexheimer, Andrew J. Davison
We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define prior…
Real-time Mapping of Physical Scene Properties with an Autonomous Robot Experimenter
Iain Haughton, Edgar Sucar, Andre Mouton +2
Neural fields can be trained from scratch to represent the shape and appearance of 3D scenes efficiently. It has also been shown that they can densely map correlated properties suc…
Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding
Kirill Mazur, Edgar Sucar, Andrew J. Davison
General scene understanding for robotics requires flexible semantic representation, so that novel objects and structures which may not have been known at training time can be ident…
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…
Simultaneous Localisation and Mapping with Quadric Surfaces
Tristan Laidlow, Andrew J. Davison
There are many possibilities for how to represent the map in simultaneous localisation and mapping (SLAM). While sparse, keypoint-based SLAM systems have achieved impressive levels…