19 citations · 26 across the 16 of their papers we have counts for
16 papers
MatchFusion: Explicit-Implicit Instance Matching for Spatio-Temporal Multimodal Autonomous Driving
Xiaoyu Li, Jiajia Fu, Long Shi +7
Sparse instance representations provide a compact interface for spatial LiDAR-camera and temporal past-current interaction in multimodal perception and E2EAD. Effective interaction…
LiDAR Prompted Spatio-Temporal Multi-View Stereo for Autonomous Driving
Qihao Sun, Jiarun Liu, Ziqian Ni +5
Accurate metric depth is critical for autonomous driving perception and simulation, yet current approaches struggle to achieve high metric accuracy, multi-view and temporal consist…
Fusion-Poly: A Polyhedral Framework Based on Spatial-Temporal Fusion for 3D Multi-Object Tracking
Xian Wu, Yitao Wu, Xiaoyu Li +3
LiDAR-camera 3D multi-object tracking (MOT) combines rich visual semantics with accurate depth cues to improve trajectory consistency and tracking reliability. In practice, however…
MipSLAM: Alias-Free Gaussian Splatting SLAM
Yingzhao Li, Yan Li, Shixiong Tian +3
This paper introduces MipSLAM, a frequency-aware 3D Gaussian Splatting (3DGS) SLAM framework capable of high-fidelity anti-aliased novel view synthesis and robust pose estimation u…
Offline-Poly: A Polyhedral Framework For Offline 3D Multi-Object Tracking
Xiaoyu Li, Yitao Wu, Xian Wu +3
Offline 3D multi-object tracking (MOT) is a critical component of the 4D auto-labeling (4DAL) process. It enhances pseudo-labels generated by high-performance detectors through the…
Rethinking the Spatio-Temporal Alignment of End-to-End 3D Perception
Xiaoyu Li, Peidong Li, Xian Wu +7
Spatio-temporal alignment is crucial for temporal modeling of end-to-end (E2E) perception in autonomous driving (AD), providing valuable structural and textural prior information.…
Adaptive-LIO: Enhancing Robustness and Precision through Environmental Adaptation in LiDAR Inertial Odometry
Chengwei Zhao, Kun Hu, Jie Xu +5
The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, LiDAR inertial odometry…
Selective Kalman Filter: When and How to Fuse Multi-Sensor Information to Overcome Degeneracy in SLAM
Jie Xu, Guanyu Huang, Wenlu Yu +5
Research trends in SLAM systems are now focusing more on multi-sensor fusion to handle challenging and degenerative environments. However, most existing multi-sensor fusion SLAM me…
RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework
Xiaoyu Li, Peidong Li, Lijun Zhao +5
3D Multi-Object Tracking (MOT) obtains significant performance improvements with the rapid advancements in 3D object detection, particularly in cost-effective multi-camera setups.…
I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry
Wenlu Yu, Jie Xu, Chengwei Zhao +5
LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization method…
Fast-Poly: A Fast Polyhedral Framework For 3D Multi-Object Tracking
Xiaoyu Li, Dedong Liu, Yitao Wu +3
3D Multi-Object Tracking (MOT) captures stable and comprehensive motion states of surrounding obstacles, essential for robotic perception. However, current 3D trackers face issues…
OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes
Tao Xie, Kun Dai, Siyi Lu +7
In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task. We propose OFVL-MS, a uni…
Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking
Xiaoyu Li, Tao Xie, Dedong Liu +5
3D Multi-object tracking (MOT) empowers mobile robots to accomplish well-informed motion planning and navigation tasks by providing motion trajectories of surrounding objects. Howe…
OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching
Kun Dai, Tao Xie, Ke Wang +3
Local feature matching is an essential component in many visual applications. In this work, we propose OAMatcher, a Tranformer-based detector-free method that imitates humans behav…
DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching
Tao Xie, Kun Dai, Ke Wang +2
Local feature matching between images remains a challenging task, especially in the presence of significant appearance variations, e.g., extreme viewpoint changes. In this work, we…
Joint Learning of Discriminative Low-dimensional Image Representations Based on Dictionary Learning and Two-layer Orthogonal Projections
Xian Wei, Hao Shen, Yuanxiang Li +4
There are some inadequacies in the language description of this paper that require further improvement. This paper is based on a revision of a conference paper. It is now necessary…