Search Results for author: Chris Xiaoxuan Lu

Found 42 papers, 20 papers with code

Click to Grasp: Zero-Shot Precise Manipulation via Visual Diffusion Descriptors

no code implementations21 Mar 2024 Nikolaos Tsagkas, Jack Rome, Subramanian Ramamoorthy, Oisin Mac Aodha, Chris Xiaoxuan Lu

Precise manipulation that is generalizable across scenes and objects remains a persistent challenge in robotics.

ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric Thermal Images

no code implementations14 Mar 2024 Fangqiang Ding, Lawrence Zhu, Xiangyu Wen, Gaowen Liu, Chris Xiaoxuan Lu

In this work, we present ThermoHands, a new benchmark for thermal image-based egocentric 3D hand pose estimation, aimed at overcoming challenges like varying lighting conditions and obstructions (e. g., handwear).

3D Hand Pose Estimation

Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities

1 code implementation7 Mar 2024 Kaiwen Cai, Zhekai Duan, Gaowen Liu, Charles Fleming, Chris Xiaoxuan Lu

Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotation, and computational constraints remain.

Contrastive Learning Knowledge Distillation +1

Multimodal Indoor Localization Using Crowdsourced Radio Maps

no code implementations17 Nov 2023 Zhaoguang Yi, Xiangyu Wen, Qiyue Xia, Peize Li, Francisco Zampella, Firas Alsehly, Chris Xiaoxuan Lu

Indoor Positioning Systems (IPS) traditionally rely on odometry and building infrastructures like WiFi, often supplemented by building floor plans for increased accuracy.

Indoor Localization

Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation

1 code implementation29 Sep 2023 Jianning Deng, Gabriel Chan, Hantao Zhong, Chris Xiaoxuan Lu

Specifically, spatial alignment is proposed to deal with the geometry discrepancy for better instance matching between LiDAR and radar.

Attribute Hallucination +3

Deep Learning for Visual Localization and Mapping: A Survey

no code implementations27 Aug 2023 Changhao Chen, Bing Wang, Chris Xiaoxuan Lu, Niki Trigoni, Andrew Markham

Deep learning based localization and mapping approaches have recently emerged as a new research direction and receive significant attentions from both industry and academia.

Simultaneous Localization and Mapping Visual Localization +1

Risk Controlled Image Retrieval

no code implementations14 Jul 2023 Kaiwen Cai, Chris Xiaoxuan Lu, Xingyu Zhao, Xiaowei Huang

Most image retrieval research focuses on improving predictive performance, ignoring scenarios where the reliability of the prediction is also crucial.

Image Retrieval Model Selection +2

Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion

1 code implementation7 Jul 2023 Kaiwen Cai, Qiyue Xia, Peize Li, John Stankovic, Chris Xiaoxuan Lu

The majority of human detection methods rely on the sensor using visible lights (e. g., RGB cameras) but such sensors are limited in scenarios with degraded vision conditions.

Human Detection

milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing

1 code implementation29 Jun 2023 Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu

In this work, we propose milliFlow, a novel deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks.

Decision Making Gesture Recognition +4

VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations

no code implementations21 May 2023 Nikolaos Tsagkas, Oisin Mac Aodha, Chris Xiaoxuan Lu

We present Visual-Language Fields (VL-Fields), a neural implicit spatial representation that enables open-vocabulary semantic queries.

Segmentation Semantic Segmentation

Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings

1 code implementation29 Sep 2022 Kaiwen Cai, Chris Xiaoxuan Lu, Xiaowei Huang

In this work, we present CUE, a novel uncertainty estimation method for dense prediction tasks in 3D point clouds.

Metric Learning Semantic Segmentation

GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning

1 code implementation30 Aug 2022 Lang Deng, Jianfei Yang, Shenghai Yuan, Han Zou, Chris Xiaoxuan Lu, Lihua Xie

As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications.

Gait Recognition Retrieval

Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars

no code implementations16 Jul 2022 Dongjiang Cao, Ruofeng Liu, Hao Li, Shuai Wang, Wenchao Jiang, Chris Xiaoxuan Lu

Human identification is a key requirement for many applications in everyday life, such as personalized services, automatic surveillance, continuous authentication, and contact tracing during pandemics, etc.

Metric Learning Person Re-Identification

STUN: Self-Teaching Uncertainty Estimation for Place Recognition

2 code implementations3 Mar 2022 Kaiwen Cai, Chris Xiaoxuan Lu, Xiaowei Huang

Then, supervised by the pretrained teacher net, a student net with an additional variance branch is trained to finetune the embedding priors and estimate the uncertainty sample by sample.

Metric Learning Simultaneous Localization and Mapping

Self-Supervised Scene Flow Estimation with 4-D Automotive Radar

2 code implementations2 Mar 2022 Fangqiang Ding, Zhijun Pan, Yimin Deng, Jianning Deng, Chris Xiaoxuan Lu

Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy.

Autonomous Vehicles Motion Segmentation +2

Multiagent Model-based Credit Assignment for Continuous Control

no code implementations27 Dec 2021 Dongge Han, Chris Xiaoxuan Lu, Tomasz Michalak, Michael Wooldridge

By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control.

Continuous Control reinforcement-learning +1

CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals

1 code implementation7 Nov 2021 Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang, Niki Trigoni, Andrew Markham

To avoid the drawbacks of conventional DFT pre-processing, we propose a learnable pre-processing module, named CubeLearn, to directly extract features from raw radar signal and build an end-to-end deep neural network for mmWave FMCW radar motion recognition applications.

Activity Recognition

Deep Inertial Odometry with Accurate IMU Preintegration

no code implementations18 Jan 2021 Rooholla Khorrambakht, Chris Xiaoxuan Lu, Hamed Damirchi, Zhenghua Chen, Zhengguo Li

Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors.

Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference

no code implementations13 Jan 2020 Changhao Chen, Peijun Zhao, Chris Xiaoxuan Lu, Wei Wang, Andrew Markham, Niki Trigoni

Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots.

Learning Selective Sensor Fusion for States Estimation

no code implementations30 Dec 2019 Changhao Chen, Stefano Rosa, Chris Xiaoxuan Lu, Bing Wang, Niki Trigoni, Andrew Markham

By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e. g. locations and orientations.

Autonomous Vehicles Sensor Fusion

Snoopy: Sniffing Your Smartwatch Passwords via Deep Sequence Learning

1 code implementation10 Dec 2019 Chris Xiaoxuan Lu, Bowen Du, Hongkai Wen, Sen Wang, Andrew Markham, Ivan Martinovic, Yiran Shen, Niki Trigoni

Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile platforms.

See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar

1 code implementation1 Nov 2019 Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao, Bing Wang, Changhao Chen, John A. Stankovic, Niki Trigoni, Andrew Markham

This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response.

Milli-RIO: Ego-Motion Estimation with Millimetre-Wave Radar and Inertial Measurement Unit Sensor

no code implementations12 Sep 2019 Yasin Almalioglu, Mehmet Turan, Chris Xiaoxuan Lu, Niki Trigoni, Andrew Markham

With the fast-growing demand of location-based services in various indoor environments, robust indoor ego-motion estimation has attracted significant interest in the last decades.

Indoor Localization Motion Estimation +1

AtLoc: Attention Guided Camera Localization

1 code implementation8 Sep 2019 Bing Wang, Changhao Chen, Chris Xiaoxuan Lu, Peijun Zhao, Niki Trigoni, Andrew Markham

Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers.

Camera Localization Visual Localization

Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues

1 code implementation14 Aug 2019 Chris Xiaoxuan Lu, Xuan Kan, Bowen Du, Changhao Chen, Hongkai Wen, Andrew Markham, Niki Trigoni, John Stankovic

Inspired by the fact that most people carry smart wireless devices with them, e. g. smartphones, we propose to use this wireless identifier as a supervisory label.

Face Recognition

DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction

no code implementations11 Aug 2019 Changhao Chen, Chris Xiaoxuan Lu, Bing Wang, Niki Trigoni, Andrew Markham

In addition we show how DynaNet can indicate failures through investigation of properties such as the rate of innovation (Kalman Gain).

Motion Estimation Sensor Fusion +1

Selective Sensor Fusion for Neural Visual-Inertial Odometry

no code implementations CVPR 2019 Changhao Chen, Stefano Rosa, Yishu Miao, Chris Xiaoxuan Lu, Wei Wu, Andrew Markham, Niki Trigoni

Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data.

Autonomous Driving Sensor Fusion

Transferring Physical Motion Between Domains for Neural Inertial Tracking

no code implementations4 Oct 2018 Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu, Phil Blunsom, Andrew Markham, Niki Trigoni

Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent.

Domain Adaptation

OxIOD: The Dataset for Deep Inertial Odometry

no code implementations20 Sep 2018 Changhao Chen, Peijun Zhao, Chris Xiaoxuan Lu, Wei Wang, Andrew Markham, Niki Trigoni

Advances in micro-electro-mechanical (MEMS) techniques enable inertial measurements units (IMUs) to be small, cheap, energy efficient, and widely used in smartphones, robots, and drones.

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