MACalib-net: spatiotemporal multi-attention cooperative network for LiDAR-camera extrinsic calibration
TL;DR
MACalib-Net is proposed, a spatiotemporal multi-attention cooperative network for LiDAR-camera extrinsic calibration that introduces a hierarchical Local-Global Spatial Attention mechanism, which combines local window-based geometric extraction with representative-based global context modeling for fine-grained spatial feature alignment.
Abstract
Extrinsic calibration accuracy is a critical bottleneck for LiDAR-camera fusion in autonomous driving. To address motion dynamics and cross-modal disparities, this paper proposes MACalib-Net, a spatiotemporal multi-attention cooperative network for LiDAR-camera extrinsic calibration. The proposed method introduces a hierarchical Local-Global Spatial Attention mechanism, which combines local window-based geometric extraction with representative-based global context modeling for fine-grained spatial feature alignment. Furthermore, a Temporal Transformer is introduced to explicitly model inter-frame motion dependencies. Experimental results on the KITTI odometry dataset demonstrate that MACalib-Net achieves an average rotation error of 0.074 ∘ and a translation error of 0.682 cm, outperforming state-of-the-art methods in translation accuracy. Robust generalization is also demonstrated on non-repetitive scanning solid-state LiDARs (Livox Avia). Real-platform validation on a custom-built 32-line LiDAR and monocular camera system further confirms the method’s deployability, achieving rotation and translation errors of approximately 0.1∘ and 1 cm within a two-stage calibration pipeline. The code is available at: https://github.com/isjhli/MACalibNet.