Search Results for author: Edgar Sucar

Found 10 papers, 3 papers with code

Real-time Mapping of Physical Scene Properties with an Autonomous Robot Experimenter

no code implementations31 Oct 2022 Iain Haughton, Edgar Sucar, Andre Mouton, Edward Johns, Andrew J. Davison

Neural fields can be trained from scratch to represent the shape and appearance of 3D scenes efficiently.

Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding

no code implementations6 Oct 2022 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 identified, segmented and grouped.

Scene Understanding

ILabel: Interactive Neural Scene Labelling

no code implementations29 Nov 2021 Shuaifeng Zhi, Edgar Sucar, Andre Mouton, Iain Haughton, Tristan Laidlow, Andrew J. Davison

ILabel's underlying model is a multilayer perceptron (MLP) trained from scratch in real-time to learn a joint neural scene representation.

Semantic Segmentation

Incremental Abstraction in Distributed Probabilistic SLAM Graphs

no code implementations13 Sep 2021 Joseph Ortiz, Talfan Evans, Edgar Sucar, Andrew J. Davison

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 of robustly identifying abstract scene elements and optimising continually changing, complex graphs.

iMAP: Implicit Mapping and Positioning in Real-Time

3 code implementations ICCV 2021 Edgar Sucar, Shikun Liu, Joseph Ortiz, Andrew J. Davison

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.

MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion

1 code implementation CVPR 2020 Kentaro Wada, Edgar Sucar, Stephen James, Daniel Lenton, Andrew J. Davison

Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion.

6D Pose Estimation Object

NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction

no code implementations9 Apr 2020 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.

3D Object Reconstruction Object

Bayesian Scale Estimation for Monocular SLAM Based on Generic Object Detection for Correcting Scale Drift

no code implementations7 Nov 2017 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 reconstruction of the 3D map and of the camera trajectory.

Robotics

Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection

no code implementations27 May 2017 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.

Object object-detection +1

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