  Eliminating the rain degradation in stereo images poses a formidable
challenge, which necessitates the efficient exploitation of mutual information
present between the dual views. To this end, we devise MQINet, which employs
multi-dimension queries and interactions for stereo image deraining. More
specifically, our approach incorporates a context-aware dimension-wise queried
block (CDQB). This module leverages dimension-wise queries that are independent
of the input features and employs global context-aware attention (GCA) to
capture essential features while avoiding the entanglement of redundant or
irrelevant information. Meanwhile, we introduce an intra-view physics-aware
attention (IPA) based on the inverse physical model of rainy images. IPA
extracts shallow features that are sensitive to the physics of rain
degradation, facilitating the reduction of rain-related artifacts during the
early learning period. Furthermore, we integrate a cross-view multi-dimension
interacting attention mechanism (CMIA) to foster comprehensive feature
interaction between the two views across multiple dimensions. Extensive
experimental evaluations demonstrate the superiority of our model over EPRRNet
and StereoIRR, achieving respective improvements of 4.18 dB and 0.45 dB in
PSNR. Code and models are available at \url{https://github.com/chdwyb/MQINet}.
