3-D Line Matching Network Based on Matching Existence Guidance and Knowledge Distillation

In applications, such as scene reconstruction and odometry, accurate matching associations for 3-D lines are crucial. Real-world scenes introduce inconsistencies due to variations in perspective, leading to nonoverlapping data acting as noise. Accurately matching partially overlapping sets of 3-D lines becomes challenging, potentially resulting in failed scene reconstruction and erroneous positioning. Prior approaches relied on the traditional iterative closest line (ICL) methods, involving iterative calculations and sensitivity to initial poses, and were prone to matching failures in low-overlap rate data and singular pattern scenes. Existing 3-D line matching networks either did not consider the noise in 3-D line collections or failed to retain more valid matching pairs, while these models often require a larger number of parameters and inference time. To address these issues, this article proposes matching existence guidance module (MEG)-Net, a Pl & uuml;cker line matching network guided by the existence of matches. It leverages the rich geometric characteristics of 3-D lines represented as Pl & uuml;cker lines, enhancing feature robustness. By guiding the model to handle the noisy data through match existence guidance, it improves the model's performance on partially overlapping 3-D line data. Experiments on the indoor and outdoor data sets and the Out of Distribution (OOD) data sets demonstrate that the MEG-Net outperforms traditional methods and baseline models in 3-D line matching, with better scalability and noise robustness, achieving state-of-the-art results. Additionally, we propose an innovative knowledge distillation method based on the matching matrices, training a more efficient MEG-Net mini student model with approximately 70% fewer parameters and multiply accumulate operations (MACs), while maintaining superior performance and faster inference speeds on the indoor data sets.