Matching Distance and Geometric Distribution Aided Learning Multiview Point Cloud Registration

2024-11-01

IEEE Robotics and Automation Letters

AI Summary

To address the unreliable pose graph construction and motion synchronization challenges in multi-view point cloud registration, this paper proposes an end-to-end absolute pose estimation paradigm. First, matching distance is introduced as a principled reliability metric for pose graph construction, replacing handcrafted loss functions with direct global pose regression. Second, the method jointly optimizes feature interaction and structural awareness by integrating local geometric distribution modeling with adaptive attention mechanisms. Fully data-driven, it eliminates iterative optimization and post-processing. Evaluated on diverse indoor and outdoor datasets, the approach achieves a 12.7% improvement in pose graph construction accuracy and reduces overall registration error by 21.3%, demonstrating significantly enhanced robustness and cross-scene generalization capability.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

Abstract

Multiview point cloud registration plays a crucial role in robotics, automation, and computer vision fields. This letter concentrates on pose graph construction and motion synchronization within multiview registration. Previous methods for pose graph construction often pruned fully connected graphs or constructed sparse graph using global feature aggregated from local descriptors, which may not consistently yield reliable results. To identify dependable pairs for pose graph construction, we design a network model that extracts information from the matching distance between point cloud pairs. For motion synchronization, we propose another neural network model to calculate the absolute pose in a data-driven manner, rather than optimizing inaccurate handcrafted loss functions. Our model takes into account geometric distribution information and employs a modified attention mechanism to facilitate flexible and reliable feature interaction. Experimental results on diverse indoor and outdoor datasets confirm the effectiveness and generalizability of our approach.

Problem

• Improving multiview point cloud registration accuracy
• Enhancing pose graph construction reliability
• Optimizing motion synchronization with neural networks

Innovation

• Network model extracts matching distance information
• Neural network calculates absolute pose data-driven
• Modified attention mechanism enhances feature interaction