Search Results for author:
Found 11 papers, 5 papers with code
Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search
Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness.
Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior
This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice.
Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration
Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm.
HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HRegNet.
PointINet: Point Cloud Frame Interpolation Network
Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.
MoNet: Motion-based Point Cloud Prediction Network
3D point clouds accurately model 3D information of surrounding environment and are crucial for intelligent vehicles to perceive the scene.
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each sampled point and an attention mechanism to aggregate the positions and features of neighbor points.
Globally optimal vertical direction estimation in Atlanta World
Accordingly, we propose a vertical direction estimation method by considering the relationship between the vertical frame and horizontal frames.
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
In this paper, we consider pairwise constraints and propose a globally optimal algorithm for solving the absolute pose estimation problem.
Fast and Globally Optimal Rigid Registration of 3D Point Sets by Transformation Decomposition
However, the existing global methods are slow for two main reasons: the computational complexity of BnB is exponential to the problem dimensionality (which is six for 3D rigid registration), and the bound evaluation used in BnB is inefficient.
Efficient Global Point Cloud Registration by Matching Rotation Invariant Features Through Translation Search
Three-dimensional rigid point cloud registration has many applications in computer vision and robotics.
