Efficient and Robust Point Cloud Registration via Heuristics-Guided Parameter Search.

Area of Science:

• Computer Vision
• Geometric Computing
• Robotics

Background:

• Point cloud registration is vital for 3D reconstruction and analysis.
• Current methods struggle with high outlier ratios in correspondence sets.
• Robust registration techniques are essential for accurate 3D transformations.

Purpose of the Study:

• To develop a more efficient and robust method for estimating rigid transformations in 3D point cloud registration.
• To address the limitations of existing parameter search strategies in terms of speed.
• To improve the trade-off between robustness and efficiency in 3D registration.

Main Methods:

• A heuristics-guided parameter search strategy is proposed.
• The method involves sampling correspondences to define feasible search regions.
• A three-stage decomposition pipeline reparameterizes the 6D feasible region into lower-dimensional sub-problems.
• 1D interval stabbing and a valid sampling strategy are employed for acceleration.

Main Results:

• The proposed strategy significantly reduces the search space compared to traditional methods.
• It achieves comparable robustness to state-of-the-art methods.
• Experiments show a substantial improvement in efficiency (speed) on simulated and real-world datasets.

Conclusions:

• The heuristics-guided parameter search offers an excellent balance between efficiency and robustness for 3D point cloud registration.
• The decomposition pipeline and sampling strategy effectively accelerate the search process.
• This approach provides a practical solution for real-time or near-real-time 3D registration applications.