Using Multi-Level Consistency Learning for Partial-to-Partial Point Cloud Registration.

Area of Science:

• Computer Vision
• Computer Graphics
• Machine Learning

Background:

• Point cloud registration is fundamental in computer vision and graphics.
• Deep learning methods have advanced point cloud registration, but struggle with partial-to-partial scenarios.
• Existing methods face challenges in accurately aligning incomplete 3D datasets.

Purpose of the Study:

• To introduce MCLNet, a novel end-to-end framework for robust point cloud registration.
• To address the challenge of partial-to-partial registration using multi-level consistency.
• To enhance accuracy and efficiency in point cloud alignment tasks.

Main Methods:

• Developed MCLNet, an end-to-end deep learning framework utilizing multi-level consistency.
• Employed point-level consistency for pruning non-overlapping regions.
• Introduced a multi-scale attention module for correspondence-level consistency learning.
• Proposed a novel geometric consistency scheme for transformation estimation.

Main Results:

• MCLNet demonstrates strong performance on smaller-scale datasets, particularly with exact matches.
• The framework effectively handles partial-to-partial point cloud registration tasks.
• Experimental results show competitive accuracy compared to baseline methods.

Conclusions:

• MCLNet offers an effective solution for partial-to-partial point cloud registration.
• The multi-level consistency approach enhances registration accuracy and robustness.
• The method provides a balanced reference time and memory footprint, suitable for practical use.