DiffusionMVS： Multi-view stereo reconstruction algorithm for remote sensing image based on diffusion constraints

LIAN Yuanfeng; WANG Sen

doi:10.11834/jrs.20265119

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DiffusionMVS： Multi-view stereo reconstruction algorithm for remote sensing image based on diffusion constraints
Vol. 30, Issue 5, Pages: 1510-1523(2026)
Received：03 April 2025，

Published：07 May 2026
DOI： 10.11834/jrs.20265119
稿件说明：

移动端阅览

连远锋，王森.2026.DiffusionMVS：基于扩散约束的遥感影像立体重建算法.遥感学报，30（5）： 1510-1523 DOI： 10.11834/jrs.20265119.

Lian Y F and Wang S. 2026. DiffusionMVS： Multi-view stereo reconstruction algorithm for remote sensing image based on diffusion constraints. National Remote Sensing Bulletin， 30（5）：1510-1523 DOI： 10.11834/jrs.20265119.

摘要

针对遥感影像多视图立体任务中存在的特征匹配精度低、预测深度图存在噪声和边缘重建不完整等问题，本文提出一种基于扩散约束的多视图立体网络DiffusionMVS（Diffusion Multi-View Stereo）。首先，在特征金字塔网络的基础上，设计基于特征增强的多尺度特征提取模块MFE-FPN（Multi-scale Feature Enhancement Feature Pyramid Network）来增强网络学习多视图遥感影像特征的能力；其次，提出自适应特征聚合模块AFA（Adaptive Feature Aggregation）来动态整合不同层次的特征以捕获目标边缘的深度细节特征；最后，设计基于扩散约束的代价体优化模型DCM（Diffusion Constrained Module），通过优化存在噪声点的深度值分布来消除预测深度图存在的噪声干扰，并结合边缘引导的Transformer网络优化深度图边缘重建效果。实验结果显示，在WHU-TLC和LuoJia-MVS数据集测试中，与基准模型相比，本文提出的模型DiffusionMVS网络的平均绝对误差（Mean Absolute Error，MAE）指标分别提升了28.11%和3.37%，展示了较好的重建性能和泛化能力。

Abstract

Large-scale 3D scene reconstruction based on remote sensing images provides critical support for smart city development

map navigation

virtual reality

and digital twin systems. Existing 3D reconstruction algorithms predominantly rely on feature matching techniques and demonstrate satisfactory performance in small-scale or structurally simple scenes. Given the intricate terrain features and noise interference in complex or large-scale environments

significant challenges

such as suboptimal reconstruction accuracy and incomplete modeling

exist. These challenges hinder the effectiveness of these methods. Therefore

this study proposes a diffusion-constrained multiview stereo network comprising a multiscale feature enhancement feature pyramid network (MFE-FPN)

an adaptive feature aggregation module (AFA)

and a diffusion-constrained module (DCM)

to address the issues of low-feature matching accuracy

high noise in predicted depth maps

and incomplete edge reconstruction in multiview stereo for remote sensing images.

The proposed method consists of several steps. First

the network takes N multiview remote sensing images as input

with the first image serving as the reference and the remaining N-1 as source images. It adopts a three-stage coarse-to-fine strategy to predict depth maps progressively. The network utilizes the MFE-FPN module to extract multiscale features from the input images

thereby generating hierarchical feature representations. Second

the top-level features from the FPN are mapped through an edge-aware network to compute edge-aware features

which are subsequently fused with the multiscale features. Third

an AFA is designed to aggregate the multiscale features

thereby forming a matching cost volume. Fourth

a diffusion constraint module is introduced to integrate cost volume features with edge-aware features. Fifth

an edge-guided transformer is employed to enhance the representation of edge details during the denoising stage. Sixth

the cost volume features are regularized and regressed to estimate depth

resulting in the final reconstructed depth map. Seventh

an edge-aware loss function is constructed during training to preserve the edge information in the predicted depth maps effectively.

Experimental results show that compared with other methods

the DiffusionMVS network shows an improved mean absolute error metric on the WHU-TLC and LuoJia-MVS datasets by 28.11% and 3.37%

respectively

thereby demonstrating superior reconstruction performance. However

in terms of inference time

the proposed method does not achieve the best performance because of the relatively low operational efficiency of the diffusion constraint module. Nevertheless

it achieves an optimal balance between accuracy and efficiency

thereby making it highly suitable for remote sensing stereo reconstruction tasks. The results on the self-constructed dataset of oil and gas stations verify the model’s capability to reconstruct detailed geometric features. This capability benefits from the model’s excellent performance in edge preservation and generalization in unseen scenarios. Moreover

ablation experiment results confirm that the proposed MFE-FPN

AFA

and DCM modules can effectively enhance the accuracy of depth map reconstruction.

The proposed diffusion-constrained multiview stereo network significantly improves edge-processing capability and overall reconstruction accuracy through a multiscale feature enhancement module and a diffusion constraint module. Results indicate the model is well-suited for reconstructing mountains

forests

and buildings

because of its superior performance on weak-texture regions and depth map denoising challenges. It effectively addresses the reduced reconstruction accuracy of remote sensing images under noise interference. Future work will explore incorporating the Segment Anything Model into the MVS framework to leverage its rich semantic information

thereby refining the matching process and further improving reconstruction efficiency and accuracy.

关键词

Keywords

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