Search Results for author:
Found 6 papers, 1 papers with code
Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture
We believe that the combination is complementary and able to solve the inherent difficulties of using one modality input, including occlusions, extreme lighting/texture, and out-of-view for visual mocap and global drifts for inertial mocap.
Ranked #1 on
3D Human Pose Estimation
on AIST++
SGL: Structure Guidance Learning for Camera Localization
Camera localization is a classical computer vision task that serves various Artificial Intelligence and Robotics applications.
A Real-Time Fusion Framework for Long-term Visual Localization
In this paper, we present an efficient client-server visual localization architecture that fuses global and local pose estimations to realize promising precision and efficiency.
Pose Refinement with Joint Optimization of Visual Points and Lines
High-precision camera re-localization technology in a pre-established 3D environment map is the basis for many tasks, such as Augmented Reality, Robotics and Autonomous Driving.
Retrieval and Localization with Observation Constraints
The localization pipeline is designed as a coarse-to-fine paradigm.
Visual Localization Using Semantic Segmentation and Depth Prediction
In this paper, we propose a monocular visual localization pipeline leveraging semantic and depth cues.
