160 citations · 628 across the 19 of their papers we have counts for
5 papers · 1 filter
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…
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…
SafePicking: Learning Safe Object Extraction via Object-Level Mapping
Kentaro Wada, Stephen James, Andrew J. Davison
Robots need object-level scene understanding to manipulate objects while reasoning about contact, support, and occlusion among objects. Given a pile of objects, object recognition…
ReorientBot: Learning Object Reorientation for Specific-Posed Placement
Kentaro Wada, Stephen James, Andrew J. Davison
Robots need the capability of placing objects in arbitrary, specific poses to rearrange the world and achieve various valuable tasks. Object reorientation plays a crucial role in t…