5 papers · 1 filter
GESS: Multi-cue Guided Local Feature Learning via Geometric and Semantic Synergy
Yang Yi, Xieyuanli Chen, Jinpu Zhang +2
Robust local feature detection and description are foundational tasks in computer vision. Existing methods primarily rely on single appearance cues for modeling, leading to unstabl…
TAPFormer: Robust Arbitrary Point Tracking via Transient Asynchronous Fusion of Frames and Events
Jiaxiong Liu, Zhen Tan, Jinpu Zhang +4
Tracking any point (TAP) is a fundamental yet challenging task in computer vision, requiring high precision and long-term motion reasoning. Recent attempts to combine RGB frames an…
A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry
Yang Yi, Kunqing Wang, Jinpu Zhang +4
Accurate and reliable estimation of biases of low-cost Inertial Measurement Units (IMU) is a key factor to maintain the resilience of Visual-Inertial Odometry (VIO), particularly w…
Fully Spiking Neural Networks for Unified Frame-Event Object Tracking
Jingjun Yang, Liangwei Fan, Jinpu Zhang +3
The integration of image and event streams offers a promising approach for achieving robust visual object tracking in complex environments. However, current fusion methods achieve…
Tracking Any Point with Frame-Event Fusion Network at High Frame Rate
Jiaxiong Liu, Bo Wang, Zhen Tan +3
Tracking any point based on image frames is constrained by frame rates, leading to instability in high-speed scenarios and limited generalization in real-world applications. To ove…