130 citations · 525 across the 45 of their papers we have counts for
19 papers · 1 filter
RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud
Zhijun Pan, Fangqiang Ding, Hantao Zhong +1
Mobile autonomy relies on the precise perception of dynamic environments. Robustly tracking moving objects in 3D world thus plays a pivotal role for applications like trajectory pr…
Deep Learning for Visual Localization and Mapping: A Survey
Changhao Chen, Bing Wang, Chris Xiaoxuan Lu +2
Deep learning based localization and mapping approaches have recently emerged as a new research direction and receive significant attentions from both industry and academia. Instea…
Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion
Kaiwen Cai, Qiyue Xia, Peize Li +2
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. In th…
Risk Controlled Image Retrieval
Kaiwen Cai, Chris Xiaoxuan Lu, Xingyu Zhao +1
Most image retrieval research prioritizes improving predictive performance, often overlooking situations where the reliability of predictions is equally important. The gap between…
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao +1
Human motion sensing plays a crucial role in smart systems for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predomina…
VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations
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. Our model encodes and fuses the geometry of a…
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
Fangqiang Ding, Andras Palffy, Dariu M. Gavrila +1
This work proposes a novel approach to 4D radar-based scene flow estimation via cross-modal learning. Our approach is motivated by the co-located sensing redundancy in modern auton…
GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning
Lang Deng, Jianfei Yang, Shenghai Yuan +3
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…
Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars
Dongjiang Cao, Ruofeng Liu, Hao Li +3
Human identification is a key requirement for many applications in everyday life, such as personalized services, automatic surveillance, continuous authentication, and contact trac…
STUN: Self-Teaching Uncertainty Estimation for Place Recognition
Kaiwen Cai, Chris Xiaoxuan Lu, Xiaowei Huang
Place recognition is key to Simultaneous Localization and Mapping (SLAM) and spatial perception. However, a place recognition in the wild often suffers from erroneous predictions d…
Self-Supervised Scene Flow Estimation with 4-D Automotive Radar
Fangqiang Ding, Zhijun Pan, Yimin Deng +2
Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy. While estimating the scene…
P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
Bing Wang, Changhao Chen, Zhaopeng Cui +8
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D loc…
Demo Abstract: Indoor Positioning System in Visually-Degraded Environments with Millimetre-Wave Radar and Inertial Sensors
Zhuangzhuang Dai, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu +2
Positional estimation is of great importance in the public safety sector. Emergency responders such as fire fighters, medical rescue teams, and the police will all benefit from a r…
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
Changhao Chen, Bing Wang, Chris Xiaoxuan Lu +2
Deep learning based localization and mapping has recently attracted significant attention. Instead of creating hand-designed algorithms through exploitation of physical models or g…
Learning Selective Sensor Fusion for States Estimation
Changhao Chen, Stefano Rosa, Chris Xiaoxuan Lu +3
Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mob…
AtLoc: Attention Guided Camera Localization
Bing Wang, Changhao Chen, Chris Xiaoxuan Lu +3
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. To…
DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination
Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Chris Xiaoxuan Lu +7
Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fa…
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…
Selective Sensor Fusion for Neural Visual-Inertial Odometry
Changhao Chen, Stefano Rosa, Yishu Miao +4
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…