LiteMatch: Lightweight Zero-Shot Stereo Matching via Cost Volume Stabilization

Despite rapid progress in learning-based stereo matching,high accuracy is often achieved at the cost of heavy backbones andcomputationally intensive 3D cost volume processing, resulting in sub-stantial memory and runtime overhead. More critically, these methodsfrequently struggle to generalize across domains, limiting their practi-cal deployment. We present LiteMatch, a lightweight stereo matchingframework that achieves strong zero-shot generalization through costvolume stabilization-without expensive 3D convolutions. LiteMatch em-ploys two complementary encoders: a Cross-View Correspondence En-coder (CVCE) to capture global cross-view interactions, and a High-Frequency Encoder (HFE) that enhances fine structural details via FFT-based frequency cues. To stabilize the cost volume, we introduce the CostVolume Consistency Loss (CVC-Loss), a voxel-wise binary cross-entropyobjective applied to softmax-normalized cost distributions. By encour-aging sharp and unimodal disparity probabilities, CVC-Loss promotesstable cost distributions and enables rapid convergence. A lightweightrefinement module further produces sharp full-resolution disparities withlow-iteration updates, avoiding heavy recurrent refinement. With a flexi-ble design ranging from 3.36M to 9.58M parameters, LiteMatch achievesexceptional zero-shot generalization, delivering competitive EPE and D1performance across Scene Flow, KITTI, Middlebury, ETH3D, and Driv-ingStereo. Our results establish that lightweight architectures can indeedgeneralize across domains without sacrificing accuracy. Code