Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration

TL;DR

This paper introduces a deep semantic graph matching approach for large-scale outdoor point cloud registration, leveraging semantic information and graph neural networks to improve accuracy over existing methods.

Contribution

The paper proposes a novel deep semantic graph matching method that integrates semantic segmentation, graph convolutional networks, and optimal transport for improved outdoor point cloud registration.

Findings

Outperforms state-of-the-art registration methods on KITTI dataset

Effectively utilizes semantic and geometric information for registration

Enhances registration accuracy with attention mechanisms

Abstract

Current point cloud registration methods are mainly based on local geometric information and usually ignore the semantic information contained in the scenes. In this paper, we treat the point cloud registration problem as a semantic instance matching and registration task, and propose a deep semantic graph matching method (DeepSGM) for large-scale outdoor point cloud registration. Firstly, the semantic categorical labels of 3D points are obtained using a semantic segmentation network. The adjacent points with the same category labels are then clustered together using the Euclidean clustering algorithm to obtain the semantic instances, which are represented by three kinds of attributes including spatial location information, semantic categorical information, and global geometric shape information. Secondly, the semantic adjacency graph is constructed based on the spatial adjacency relations of semantic instances. To fully explore the topological structures between semantic instances in the same scene and across different scenes, the spatial distribution features and the semantic categorical features are learned with graph convolutional networks, and the global geometric shape features are learned with a PointNet-like network. These three kinds of features are further enhanced with the self-attention and cross-attention mechanisms. Thirdly, the semantic instance matching is formulated as an optimal transport problem, and solved through an optimal matching layer. Finally, the geometric transformation matrix between two point clouds is first estimated by the SVD algorithm and then refined by the ICP algorithm. Experimental results conducted on the KITTI Odometry dataset demonstrate that the proposed method improves the registration performance and outperforms various state-of-the-art methods.