Stereo Matching with Cost Volume based Sparse Disparity Propagation

TL;DR

This paper introduces a novel feature disparity propagation scheme that refines stereo matching by leveraging sparse feature points and a robust cost measure, achieving competitive results on benchmark datasets.

Contribution

The paper proposes a new disparity propagation method based on sparse feature points and a robust cost measure, enhancing stereo matching accuracy without complex cost aggregation.

Findings

Achieves performance comparable to state-of-the-art methods on Middlebury benchmark.

Introduces a $ ho$-Census cost measure for robustness in cost volume.

Demonstrates effectiveness of sparse disparity propagation in stereo matching.

Abstract

Stereo matching is crucial for binocular stereo vision. Existing methods mainly focus on simple disparity map fusion to improve stereo matching, which require multiple dense or sparse disparity maps. In this paper, we propose a simple yet novel scheme, termed feature disparity propagation, to improve general stereo matching based on matching cost volume and sparse matching feature points. Specifically, our scheme first calculates a reliable sparse disparity map by local feature matching, and then refines the disparity map by propagating reliable disparities to neighboring pixels in the matching cost domain. In addition, considering the gradient and multi-scale information of local disparity regions, we present a $\rho$-Census cost measure based on the well-known AD-Census, which guarantees the robustness of cost volume even without the cost aggregation step. Extensive experiments on Middlebury stereo benchmark V3 demonstrate that our scheme achieves promising performance comparable to state-of-the-art methods.