Research on self-cross transformer model of point cloud change detecter

TL;DR

This paper introduces a novel self-cross transformer model designed specifically for change detection in 3D point clouds, aiming to improve accuracy by directly analyzing raw point cloud data rather than relying on traditional or 2D methods.

Contribution

The paper proposes a new self-cross transformer module tailored for 3D point cloud change detection, and develops a network that directly processes point cloud data for more effective change identification.

Findings

The proposed model outperforms traditional threshold methods.

The network effectively detects changes directly from raw point clouds.

Experimental results demonstrate improved accuracy in change detection.

Abstract

With the vigorous development of the urban construction industry, engineering deformation or changes often occur during the construction process. To combat this phenomenon, it is necessary to detect changes in order to detect construction loopholes in time, ensure the integrity of the project and reduce labor costs. Or the inconvenience and injuriousness of the road. In the study of change detection in 3D point clouds, researchers have published various research methods on 3D point clouds. Directly based on but mostly based ontraditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are to convert 3D point clouds into DSM, which loses a lot of original information. Although deep learning is used in remote sensing methods, in terms of change detection of 3D point clouds, it is more converted into two-dimensional patches, and neural networks are rarely applied directly. We prefer that the network is given at the level of pixels or points. Variety. Therefore, in this article, our network builds a network for 3D point cloud change detection, and proposes a new module Cross transformer suitable for change detection. Simultaneously simulate tunneling data for change detection, and do test experiments with our network.