Iterative Volume Fusion for Asymmetric Stereo Matching

• URL: http://arxiv.org/abs/2508.09543v2
• Date: Thu, 14 Aug 2025 14:26:11 GMT
• Title: Iterative Volume Fusion for Asymmetric Stereo Matching
• Authors: Yuanting Gao, Linghao Shen,
• Abstract summary: We propose a two-phase Iterative Volume Fusion network for Asymmetric Stereo matching (IVF-AStereo)<n>Our method excels in asymmetric scenarios and shows robust performance against significant visual asymmetry.
• Score: 0.25782420501870285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stereo matching is vital in 3D computer vision, with most algorithms assuming symmetric visual properties between binocular visions. However, the rise of asymmetric multi-camera systems (e.g., tele-wide cameras) challenges this assumption and complicates stereo matching. Visual asymmetry disrupts stereo matching by affecting the crucial cost volume computation. To address this, we explore the matching cost distribution of two established cost volume construction methods in asymmetric stereo. We find that each cost volume experiences distinct information distortion, indicating that both should be comprehensively utilized to solve the issue. Based on this, we propose the two-phase Iterative Volume Fusion network for Asymmetric Stereo matching (IVF-AStereo). Initially, the aggregated concatenation volume refines the correlation volume. Subsequently, both volumes are fused to enhance fine details. Our method excels in asymmetric scenarios and shows robust performance against significant visual asymmetry. Extensive comparative experiments on benchmark datasets, along with ablation studies, confirm the effectiveness of our approach in asymmetric stereo with resolution and color degradation.

Related papers

• Integrating Disparity Confidence Estimation into Relative Depth Prior-Guided Unsupervised Stereo Matching [55.784713740698365]
  Unsupervised stereo matching has garnered significant attention for its independence from costly disparity annotations.<n>A feasible solution lies in transferring 3D geometric knowledge from a relative depth map to the stereo matching networks.<n>This work proposes a novel unsupervised learning framework to address these challenges.
  arXiv  Detail & Related papers  (2025-08-02T09:11:05Z)
• Mono2Stereo: A Benchmark and Empirical Study for Stereo Conversion [88.67015254278859]
  We introduce the Mono2Stereo dataset, providing high-quality training data and benchmark to support in-depth exploration of stereo conversion.<n>We conduct an empirical study that yields two primary findings. 1) The differences between the left and right views are subtle, yet existing metrics consider overall pixels, failing to concentrate on regions critical to stereo effects.<n>We introduce a new evaluation metric, Stereo Intersection-over-Union, which harmonizes disparity and achieves a high correlation with human judgments on stereo effect.
  arXiv  Detail & Related papers  (2025-03-28T09:25:58Z)
• Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep [31.261772846687297]
  The proposed stereo-confidence method is built upon the idea that any shift in a stereo-image pair should be updated in a corresponding amount shift in the disparity map. By comparing the desirable and predicted disparity profiles, we can quantify the level of matching ambiguity between left and right images for confidence measurement.
  arXiv  Detail & Related papers  (2024-01-22T14:52:08Z)
• Degradation-agnostic Correspondence from Resolution-asymmetric Stereo [96.03964515969652]
  We study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. We propose to impose the consistency between two views in a feature space instead of the image space, named feature-metric consistency. We find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features.
  arXiv  Detail & Related papers  (2022-04-04T12:24:34Z)
• Stereo Matching with Cost Volume based Sparse Disparity Propagation [27.74131924190943]
  We propose a simple yet novel scheme to improve general stereo matching based on matching cost volume and sparse matching feature points. Our scheme achieves promising performance comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-28T05:20:41Z)
• SMD-Nets: Stereo Mixture Density Networks [68.56947049719936]
  We propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets.
  arXiv  Detail & Related papers  (2021-04-08T16:15:46Z)
• Symmetric Parallax Attention for Stereo Image Super-Resolution [46.20494593243566]
  We improve the performance of stereo image SR by exploiting symmetry cues in stereo image pairs. We design a Siamese network equipped with a biPAM to super-resolve both sides of views. Experiments on four public datasets demonstrate the superior performance of our method.
  arXiv  Detail & Related papers  (2020-11-07T16:28:35Z)
• Parallax Attention for Unsupervised Stereo Correspondence Learning [46.035892564279564]
  Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. Recent CNN based methods commonly use cost volume techniques to capture stereo correspondence over large disparities. We propose a generic parallax-attention mechanism (PAM) to capture stereo correspondence regardless of disparity variations.
  arXiv  Detail & Related papers  (2020-09-16T01:30:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.