Extrinsic calibration algorithm for surround-view systems based on cross-attention
HUANG Shujuan
LIN Chunyu
QIN Leidong
JIN Zhiyong
ZHAO Yao
Abstract:To address the challenge of extrinsic parameter calibration for multi-camera automotive surround-view systems,this paper proposes an extrinsic parameter calibration algorithm based on a cross-attention mechanism.First,multi-scale features from multi-view images are independently extracted using residual convolutional modules to capture fine-grained image details.Then,a cross-attention module is introduced to learn global features of each camera image as well as the inter-camera feature relationships with surrounding cameras,thereby enhancing the overall feature representation capability.These features are subsequently integrated via a feature fusion module,which combines outputs from both the residual convolutional and cross-attention modules to regress the extrinsic parameters.Finally,the proposed model is validated on two datasets through perfor-mance evaluation and ablation studies.Experimental results demonstrate that,compared with existing extrinsic parameter calibration algorithms based on lane lines and textures,the algorithm proposed in this paper has better generalization and robustness in different environments,with significant improve-ments in performance metrics and bird's-eye view stitching visualization results.Compared with exist-ing extrinsic parameter calibration algorithms based on lane markings and texture cues,the proposed algorithm exhibits superior generalization and robustness across diverse environments,with notable improvements in quantitative performance metrics and bird's-eye view stitching quality.Specifically,the algorithm achieves absolute reprojection and photometric errors of 3.1 and 16.7,respectively,representing improvements of 8.82%and 8.74%over the current state-of-the-art weakly-supervised extrinsic self-calibration network(WESNet).The research findings provide technical support for online extrinsic parameter calibration in automotive surround-view systems.
Keywords:surround-view systemdeep learningcross-attentionextrinsic calibration
Publication Date:2025-06-30
Online Publishing Date:2025-08-21(First online date of this platform, not the publication date of the document)
Pages:10( 137-146 )
Journal of Beijing Jiaotong University

Journal of Beijing Jiaotong University

ISTICPKUCSCD
ISSN:1673-0291
Year, Vol.(Issue):2025,49(3)