962 citations · 1.3k across the 18 of their papers we have counts for
8 papers · 1 filter
Fusion of Radio and Camera Sensor Data for Accurate Indoor Positioning
Savvas Papaioannou, Hongkai Wen, Andrew Markham +1
Indoor positioning systems have received a lot of attention recently due to their importance for many location-based services, e.g. indoor navigation and smart buildings. Lightweig…
High Speed Rotation Estimation with Dynamic Vision Sensors
Guangrong Zhao, Yiran Shen, Ning Chen +3
Rotational speed is one of the important metrics to be measured for calibrating the electric motors in manufacturing, monitoring engine during car repairing, faults detection on el…
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Chris Xiaoxuan Lu, Xuan Kan, Bowen Du +5
Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, fa…
Dense 3D Object Reconstruction from a Single Depth View
Bo Yang, Stefano Rosa, Andrew Markham +2
In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversar…
DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
Sen Wang, Ronald Clark, Hongkai Wen +1
This paper studies monocular visual odometry (VO) problem. Most of existing VO algorithms are developed under a standard pipeline including feature extraction, feature matching, mo…
3D Object Reconstruction from a Single Depth View with Adversarial Learning
Bo Yang, Hongkai Wen, Sen Wang +3
In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial…
VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
Ronald Clark, Sen Wang, Andrew Markham +2
Machine learning techniques, namely convolutional neural networks (CNN) and regression forests, have recently shown great promise in performing 6-DoF localization of monocular imag…
VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
Ronald Clark, Sen Wang, Hongkai Wen +2
In this paper we present an on-manifold sequence-to-sequence learning approach to motion estimation using visual and inertial sensors. It is to the best of our knowledge the first…