A Unified Stereo Geometry Estimation Framework for Disparity and Surface Normal

2026-07-27

AI Summary

This work addresses the challenge of unreliable disparity and surface normal estimation in complex scenarios—such as low-light conditions, highly reflective surfaces, and transparent objects—where existing feedforward stereo matching methods suffer from insufficient geometric priors. To overcome this limitation, we propose GeoStereo, a novel framework that introduces diffusion models to stereo geometric estimation for the first time. GeoStereo establishes a bidirectional guidance mechanism between disparity and surface normals through disparity-guided normal initialization and warping-based left-view alignment conditioning, enabling their joint optimization. Evaluated under unsupervised settings, our method significantly enhances geometric reconstruction robustness, achieving state-of-the-art zero-shot disparity estimation performance on KITTI and NYUv2, and delivering leading surface normal accuracy on real-world indoor benchmarks including iBims-1 and ScanNet.

Abstract

Stereo matching and surface normal estimation are fundamental tasks in 3D vision. However, existing feed-forward stereo methods still struggle to produce reliable predictions in challenging regions, mainly due to the lack of strong geometric priors. In this paper, we propose $\textbf{GeoStereo}$, a unified stereo geometry estimation framework that leverages powerful diffusion priors to jointly predict disparity and surface normals. Specifically, GeoStereo couples a feed-forward stereo matching pipeline with a diffusion-based normal estimation branch. To enable effective interaction between the two tasks, we introduce a disparity to normal initialization strategy and construct a warp to left-view condition for the diffusion process. This coupled design allows the diffusion branch to provide strong structural priors that enhance disparity estimation in ill-posed regions, while the feed-forward branch offers reliable geometric guidance for accurate normal prediction. Extensive experiments show that GeoStereo performs reliably in challenging scenarios, including low-light environments, highly reflective surfaces, and transparent objects. Under zero-shot settings, it achieves Rank-1 disparity estimation on multiple benchmarks, including KITTI and NYUv2, and delivers the best normal estimation accuracy on many real indoor benchmarks, such as iBims-1 and ScanNet. Project page: https://qz-wei.github.io/GeoStereo.github.io/

Problem

• stereo matching
• surface normal estimation
• disparity estimation
• geometric priors
• 3D vision

Innovation

• diffusion prior
• stereo matching
• surface normal estimation
• geometric priors
• unified framework