39 citations · 85 across the 13 of their papers we have counts for
13 papers
Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation
Runze Chen, Haiyong Luo, Fang Zhao +4
Monocular depth estimation, enabled by self-supervised learning, is a key technique for 3D perception in computer vision. However, it faces significant challenges in real-world sce…
Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge
Mingyu Xiao, Runze Chen, Haiyong Luo +3
Map-free relocalization technology is crucial for applications in autonomous navigation and augmented reality, but relying on pre-built maps is often impractical. It faces signific…
CSS: Overcoming Pose and Scene Challenges in Crowd-Sourced 3D Gaussian Splatting
Runze Chen, Mingyu Xiao, Haiyong Luo +5
We introduce Crowd-Sourced Splatting (CSS), a novel 3D Gaussian Splatting (3DGS) pipeline designed to overcome the challenges of pose-free scene reconstruction using crowd-sourced…
OccupancyDETR: Using DETR for Mixed Dense-sparse 3D Occupancy Prediction
Yupeng Jia, Jie He, Runze Chen +2
Visual-based 3D semantic occupancy perception is a key technology for robotics, including autonomous vehicles, offering an enhanced understanding of the environment by 3D. This app…
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
Lingdong Kong, Yaru Niu, Shaoyuan Xie +39
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical a…
Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning
Chenxing Wang, Fang Zhao, Haichao Zhang +3
Travel Time Estimation (TTE) is indispensable in intelligent transportation system (ITS). It is significant to achieve the fine-grained Trajectory-based Travel Time Estimation (TTT…
Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network
Yuchen Fang, Yanjun Qin, Haiyong Luo +4
Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate tempora…
CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting
Yuchen Fang, Yanjun Qin, Haiyong Luo +4
Traffic forecasting is important in intelligent transportation systems of webs and beneficial to traffic safety, yet is very challenging because of the complex and dynamic spatio-t…
DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting
Yanjun Qin, Yuchen Fang, Haiyong Luo +2
Traffic forecasting is a problem of intelligent transportation systems (ITS) and crucial for individuals and public agencies. Therefore, researches pay great attention to deal with…
STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting
Yuchen Fang, Yanjun Qin, Haiyong Luo +2
Traffic prediction has gradually attracted the attention of researchers because of the increase in traffic big data. Therefore, how to mine the complex spatio-temporal correlations…
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation
Runze Chen, Haiyong Luo, Fang Zhao +3
Inertial sensor-based human activity recognition (HAR) is the base of many human-centered mobile applications. Deep learning-based fine-grained HAR models enable accurate classific…
Lvio-Fusion: A Self-adaptive Multi-sensor Fusion SLAM Framework Using Actor-critic Method
Yupeng Jia, Haiyong Luo, Fang Zhao +5
State estimation with sensors is essential for mobile robots. Due to different performance of sensors in different environments, how to fuse measurements of various sensors is a pr…
Discriminative Learning-based Smartphone Indoor Localization
Jose Luis V. Carrera, Zhongliang Zhao, Torsten Braun +2
Due to the growing area of ubiquitous mobile applications, indoor localization of smartphones has become an interesting research topic. Most of the current indoor localization syst…