18 citations · 33 across the 8 of their papers we have counts for
10 papers
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…
iSDF: Real-Time Neural Signed Distance Fields for Robot Perception
Joseph Ortiz, Alexander Clegg, Jing Dong +4
We present iSDF, a continual learning system for real-time signed distance field (SDF) reconstruction. Given a stream of posed depth images from a moving camera, it trains a random…
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…
Incremental Abstraction in Distributed Probabilistic SLAM Graphs
Joseph Ortiz, Talfan Evans, Edgar Sucar +1
Scene graphs represent the key components of a scene in a compact and semantically rich way, but are difficult to build during incremental SLAM operation because of the challenges…
iMAP: Implicit Mapping and Positioning in Real-Time
Edgar Sucar, Shikun Liu, Joseph Ortiz +1
We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera. Our network is trai…
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion
Kentaro Wada, Edgar Sucar, Stephen James +2
Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion. Recognized preci…
NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction
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. We present efficient and optimisable multi-c…
Bayesian Scale Estimation for Monocular SLAM Based on Generic Object Detection for Correcting Scale Drift
Edgar Sucar, Jean-Bernard Hayet
This work proposes a new, online algorithm for estimating the local scale correction to apply to the output of a monocular SLAM system and obtain an as faithful as possible metric…
Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
Edgar Sucar, Jean-Bernard Hayet
This paper proposes a novel method to estimate the global scale of a 3D reconstructed model within a Kalman filtering-based monocular SLAM algorithm. Our Bayesian framework integra…