TS-SatMVSNet: Slope Aware Height Estimation for Large-Scale Earth Terrain Multi-view Stereo

2025-01-02

AI Summary

Learning-based multi-view stereo (MVS) methods suffer from degraded accuracy in large-scale terrain elevation estimation due to their neglect of surface slope characteristics. To address this, we propose an end-to-end slope-aware height estimation network. Our key contributions are: (1) the first height–slope joint modeling mechanism based on height-map differentiation; (2) a micro-scale slope-guided depth interval partitioning module and a macro-scale learnable Gaussian smoothing refinement module; and (3) a slope-direction consistency loss that implicitly enforces geometric plausibility of estimated elevations. Evaluated on the WHU-TLC and MVS3D benchmarks, our method achieves state-of-the-art performance, significantly improving both absolute elevation accuracy and cross-scene generalization capability for large-area remote sensing terrain reconstruction.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

3D terrain reconstruction with remote sensing imagery achieves cost-effective and large-scale earth observation and is crucial for safeguarding natural disasters, monitoring ecological changes, and preserving the environment.Recently, learning-based multi-view stereo~(MVS) methods have shown promise in this task. However, these methods simply modify the general learning-based MVS framework for height estimation, which overlooks the terrain characteristics and results in insufficient accuracy. Considering that the Earth's surface generally undulates with no drastic changes and can be measured by slope, integrating slope considerations into MVS frameworks could enhance the accuracy of terrain reconstructions. To this end, we propose an end-to-end slope-aware height estimation network named TS-SatMVSNet for large-scale remote sensing terrain reconstruction.To effectively obtain the slope representation, drawing from mathematical gradient concepts, we innovatively proposed a height-based slope calculation strategy to first calculate a slope map from a height map to measure the terrain undulation. To fully integrate slope information into the MVS pipeline, we separately design two slope-guided modules to enhance reconstruction outcomes at both micro and macro levels. Specifically, at the micro level, we designed a slope-guided interval partition module for refined height estimation using slope values. At the macro level, a height correction module is proposed, using a learnable Gaussian smoothing operator to amend the inaccurate height values. Additionally, to enhance the efficacy of height estimation, we proposed a slope direction loss for implicitly optimizing height estimation results. Extensive experiments on the WHU-TLC dataset and MVS3D dataset show that our proposed method achieves state-of-the-art performance and demonstrates competitive generalization ability.

Problem

• Multi-View Stereo
• Terrain Estimation
• Learning Techniques

Innovation

• TS-SatMVSNet
• Gradient-based Slope Perception
• Direction-aware Loss