Cross-Modal Optical Flow Estimation via Modality Compensation and Alignment

Cross-modal optical flow estimation aims to predict motion fields between two frames collected from different modalities, recently attracting intensive attention. However, a substantial yet challenging problem is how to match images across a large modal discrepancy. In this paper, we propose a modality compensation module (MCM) to extract complementary features from different modalities adaptively. Moreover, a cross-modal feature alignment loss is introduced into our network, pulling the compensative features of two cross-modal frames closer and effectively reducing the modal discrepancy. The experimental results demonstrate that our method can achieve competitive performance on the cross-modal optical flow dataset CrossKITTI. Moreover, we experimentally verify that the proposed MCM and cross-modal feature alignment loss are effective for cross-modal optical flow estimation.