Deep S2P: Integrating Learning Based Stereo Matching Into the Satellite Stereo Pipeline

• URL: http://arxiv.org/abs/2603.21882v1
• Date: Mon, 23 Mar 2026 12:11:17 GMT
• Title: Deep S2P: Integrating Learning Based Stereo Matching Into the Satellite Stereo Pipeline
• Abstract summary: Digital Surface Model generation from satellite imagery is a core task in Earth observation.<n>Recent learning-based stereo matchers achieve state-of-the-art performance on standard benchmarks.<n>We integrate several modern learning-based stereo matchers, including StereoAnywhere, MonSter, Foundation Stereo, and a satellite fine-tuned variant of MonSter, into the Satellite Stereo Pipeline.
• Score: 18.262323712986973
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital Surface Model generation from satellite imagery is a core task in Earth observation and is commonly addressed using classical stereoscopic matching algorithms in satellite pipelines as in the Satellite Stereo Pipeline (S2P). While recent learning-based stereo matchers achieve state-of-the-art performance on standard benchmarks, their integration into operational satellite pipelines remains challenging due to differences in viewing geometry and disparity assumptions. In this work, we integrate several modern learning-based stereo matchers, including StereoAnywhere, MonSter, Foundation Stereo, and a satellite fine-tuned variant of MonSter, into the Satellite Stereo Pipeline, adapting the rectification stage to enforce compatible disparity polarity and range. We release the corresponding code to enable reproducible use of these methods in large-scale Earth observation workflows. Experiments on satellite imagery show consistent improvements over classical cost-volume-based approaches in terms of Digital Surface Model accuracy, although commonly used metrics such as mean absolute error exhibit saturation effects. Qualitative results reveal substantially improved geometric detail and sharper structures, highlighting the need for evaluation strategies that better reflect perceptual and structural fidelity. At the same time, performance over challenging surface types such as vegetation remains limited across all evaluated models, indicating open challenges for learning-based stereo in natural environments.

Related papers

• Multi-modal, multi-scale representation learning for satellite imagery analysis just needs a good ALiBi [0.0]
  This paper presents Scale-ALiBi, a linear bias transformer attention mechanism with a spatial encoding bias to relationships between image patches at different ground sample distance scales.<n>We provide an implementation of Scale-ALiBi over a dataset of aligned high- and low-resolution optical and low-resolution SAR satellite imagery data using a triple-contrastive and reconstructive architecture.
  arXiv  Detail & Related papers  (2026-04-11T20:55:28Z)
• Stereo-Inertial Poser: Towards Metric-Accurate Shape-Aware Motion Capture Using Sparse IMUs and a Single Stereo Camera [54.967647497048205]
  We present Stereo-Inertial Poser, a real-time motion capture system that estimates metric-accurate and shape-aware 3D human motion.<n>We replace the monocular RGB with stereo vision, enabling direct 3D keypoint extraction and body shape parameter estimation.<n>Our method produces drift-free global translation under a long recording time and reduces foot-skating effects.
  arXiv  Detail & Related papers  (2026-03-02T17:46:38Z)
• StereoAdapter-2: Globally Structure-Consistent Underwater Stereo Depth Estimation [18.410248448681514]
  We propose StereoAdapter-2, which replaces the conventional ConvGRU updater with a novel ConvSS2D operator.<n>We construct UW-StereoDepth-80K, a large-scale synthetic underwater stereo dataset.<n>Our framework achieves state-of-the-art zero-shot performance on underwater benchmarks.
  arXiv  Detail & Related papers  (2026-02-18T22:12:08Z)
• SonarSweep: Fusing Sonar and Vision for Robust 3D Reconstruction via Plane Sweeping [6.826863809223021]
  Single-modality approaches to 3D reconstruction fail due to poor visibility and geometric constraints.<n>Prior fusion technique relies on flawed geometrics, leading to significant artifacts and an inability to model complex scenes.<n>In this paper, we introduce SonarSweep, a novel, end-to-end deep learning framework that overcomes these limitations.
  arXiv  Detail & Related papers  (2025-11-01T04:12:27Z)
• Satellite to GroundScape -- Large-scale Consistent Ground View Generation from Satellite Views [5.146618378243241]
  We propose a novel cross-view synthesis approach designed to ensure consistency across ground-view images generated from satellite views.<n>Our method, based on a fixed latent diffusion model, introduces two conditioning modules: satellite-guided denoising and satellite-temporal denoising.<n>We contribute a large-scale satellite-ground dataset containing over 100,000 perspective pairs to facilitate extensive ground scene or video generation.
  arXiv  Detail & Related papers  (2025-04-22T10:58:42Z)
• Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model [70.67610495024459]
  Camera-based setups offer a cost-effective option by using stereo depth estimation to generate dense, high-resolution depth maps.<n>Existing omnidirectional stereo matching approaches achieve only limited depth accuracy across diverse environments.<n>We present DFI-OmniStereo, a novel omnidirectional stereo matching method that leverages a large-scale pre-trained foundation model for relative monocular depth estimation.
  arXiv  Detail & Related papers  (2025-03-30T16:24:22Z)
• FoundationStereo: Zero-Shot Stereo Matching [50.79202911274819]
  FoundationStereo is a foundation model for stereo depth estimation.<n>We first construct a large-scale (1M stereo pairs) synthetic training dataset.<n>We then design a number of network architecture components to enhance scalability.
  arXiv  Detail & Related papers  (2025-01-17T01:01:44Z)
• CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View Synthesis [54.852701978617056]
  CrossViewDiff is a cross-view diffusion model for satellite-to-street view synthesis. To address the challenges posed by the large discrepancy across views, we design the satellite scene structure estimation and cross-view texture mapping modules. To achieve a more comprehensive evaluation of the synthesis results, we additionally design a GPT-based scoring method.
  arXiv  Detail & Related papers  (2024-08-27T03:41:44Z)
• View Consistent Purification for Accurate Cross-View Localization [59.48131378244399]
  This paper proposes a fine-grained self-localization method for outdoor robotics. The proposed method addresses limitations in existing cross-view localization methods. It is the first sparse visual-only method that enhances perception in dynamic environments.
  arXiv  Detail & Related papers  (2023-08-16T02:51:52Z)
• Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative Models [27.62417543307831]
  We present the first pre-trained style- and wavelet-based GAN model that can synthesize a wide gamut of realistic satellite images. We show that by analyzing the intermediate activations of our network, one can discover a multitude of interpretable semantic directions.
  arXiv  Detail & Related papers  (2022-08-03T14:19:24Z)
• ResDepth: A Deep Prior For 3D Reconstruction From High-resolution Satellite Images [28.975837416508142]
  We introduce ResDepth, a convolutional neural network that learns such an expressive geometric prior from example data. In a series of experiments, we find that the proposed method consistently improves stereo DSMs both quantitatively and qualitatively. We show that the prior encoded in the network weights captures meaningful geometric characteristics of urban design.
  arXiv  Detail & Related papers  (2021-06-15T12:51:28Z)
• 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)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)

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.