Mamba-SF: Monocular Scene Flow Learning with State Space Models

Monocular scene flow estimation has been a long-standing problem in computer vision. Methods based on the RAFT architecture are currently the mainstream approaches, while often overlooking the long-range dependencies in motion and texture features and fail to fully utilize the spatial information in texture features. In this paper, we consider that using Transformers introduces high computational complexity. Therefore, we propose the Mamba Motion Module based on State Space Models design, which first models long-range dependencies in motion and texture features and then fully leverages the spatial information in texture features to enhance the motion features, generating global motion features while maintaining low computational complexity. Additionally, texture features play a significant role in constraining motion boundaries. Therefore, we propose the Enhanced Texture Module, which predicts a set of channel weights from the global motion features to enrich the channel properties of the texture features and concatenates texture features with the global motion features along the channels, thereby improving scene flow accuracy. Experimental results show that our method achieves highly competitive results on the KITTI 2015 and Eigen Split datasets, increasing by 18.82% and 2.15% compared to the baseline, respectively.