STEREOFLOW: Progressive Stereo Matching with StereoDiT and Transition Flow Matching

2026-07-22

AI Summary

This work addresses the limitations of traditional stereo matching methods, which model disparity estimation as deterministic regression and consequently struggle with multimodal distributions and ambiguous regions, often yielding mean bias. To overcome this, the paper introduces the first generative stereo matching framework that unifies deterministic regression with probabilistic distribution modeling. The approach leverages a two-stage progressive cascaded network, a frequency-decoupled StereoDiT pixel diffusion Transformer, and a novel Transition Flow Matching optimization objective to enable efficient few-step generation. The proposed method achieves state-of-the-art performance across multiple benchmarks—including Scene Flow, KITTI, ETH3D, and Middlebury—and demonstrates exceptional zero-shot generalization, significantly improving detail fidelity and consistency in geometrically discontinuous and textureless regions.

Abstract

Stereo matching is a fundamental task in 3D reconstruction. Despite remarkable advances, the prevailing paradigms formulate stereo matching as a deterministic regression problem, collapsing the multimodal distribution modeling into a single-point estimation. This formulation suffers from a regression-to-mean bias, frequently struggling with ambiguous regions. In contrast, we introduce a prior-guided generative framework that integrates deterministic matching regression and generative distribution modeling within a complementary formulation. Built upon this formulation, we introduce StereoFlow through three key components: (i) a two-stage progressive cascade matching network that progressively produces multi-resolution stereo conditions with complementary matching cues; (ii) a pixel diffusion transformer (termed StereoDiT) with a frequency-decoupled architecture for modeling correspondence ambiguity; (iii) a few-step flow matching objective (termed Transition Flow Matching) for efficient optimization. In summary, \textsc{\textbf{StereoFlow}} achieves strong geometric consistency and rich fine-grained details in ill-posed, discontinuous regions and under zero-shot generalization. Extensive experiments demonstrate that the proposed StereoFlow establishes multiple state-of-the-art results across benchmarks, including Scene Flow, KITTI, ETH3D, and Middlebury.

Problem

• stereo matching
• multimodal distribution
• regression-to-mean bias
• ambiguous regions
• 3D reconstruction

Innovation

• StereoFlow
• StereoDiT
• Transition Flow Matching
• generative stereo matching
• frequency-decoupled architecture