While recent deep learning-based stereo matching networks have shown outstanding advances, there are still some unsolved challenges. First, most state-of-the-art stereo models employ 3D convolutions for 4D cost volume aggregation,which limit the deployment of networks for resource-limited mobile environments owing to heavy consumption of computation and memory. Second, most stereo networks indirectly supervise cost volumes through disparity regression loss by using the softargmax function. This causes problems in ambiguous regions, such as the boundaries of objects, because there are many possibilities for unreasonable cost distributions which result in overfitting problem. To address these problems, we first propose an efficient multi scale sequential feature fusion network (MSFFNet). Specifically, we connect multi-scale SFF modules in
parallel with a cross-scale fusion function to generate a set of cost volumes with different scales. These cost volumes are then effectively combined using the proposed interlaced concatenation method. Second, we propose an adaptive cost volume filtering (ACVF) loss function that directly supervises our estimated cost volume. The proposed ACVF loss directly adds constraints to the cost volume using the probability distribution generated from the ground truth disparity map and that estimated from the teacher network with higher accuracy. Results of several experiments using representative datasets for stereo matching shows that our proposed method is more efficient than previous methods. Concretely, our network architecture consumes fewer parameters and generates reasonable disparity maps with faster speed compared with the existing state-of-the art stereo models.
