6 papers
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…
HGSLoc: 3DGS-based Heuristic Camera Pose Refinement
Zhongyan Niu, Zhen Tan, Jinpu Zhang +2
Visual localization refers to the process of determining camera poses and orientation within a known scene representation. This task is often complicated by factors such as changes…
Uncertainty-Aware Normal-Guided Gaussian Splatting for Surface Reconstruction from Sparse Image Sequences
Zhen Tan, Xieyuanli Chen, Jinpu Zhang +2
3D Gaussian Splatting (3DGS) has achieved impressive rendering performance in novel view synthesis. However, its efficacy diminishes considerably in sparse image sequences, where i…