Inter-Scale Similarity Guided Cost Aggregation for Stereo Matching

Stereo matching aims to estimate 3D geometry by computing disparity from a rectified image pair. Most deep learning based stereo matching methods aggregate multi-scale cost volumes computed by downsampling and achieve good performance. However, their effectiveness in fine-grained areas is limited by significant detail loss during downsampling and the use of fixed weights in upsampling. In this paper, we propose an inter-scale similarity-guided cost aggregation method that dynamically upsamples the cost volumes according to the content of images for stereo matching. The method consists of two modules: inter-scale similarity measurement and stereo-content-aware cost aggregation. Specifically, we use inter-scale similarity measurement to generate similarity guidance from feature maps in adjacent scales. The guidance, generated from both reference and target images, is then used to aggregate the cost volumes from low-resolution to high-resolution via stereo-content-aware cost aggregation. We further split the 3D aggregation into 1D disparity and 2D spatial aggregation to reduce the computational cost. Experimental results on various benchmarks (e.g., SceneFlow, KITTI, Middlebury and ETH3D-two-view) show that our method achieves consistent performance gain on multiple models (e.g., PSM-Net, HSM-Net, CF-Net, FastAcv, and FactAcvPlus). The code can be found at https://github.com/Pengxiang-Li/issga-stereo.