A-SATMVSNet: An attention-aware multi-view stereo matching network based on satellite imagery

Abstract

Introduction: The stereo matching technology of satellite imagery is an important way to reconstruct real world. Most stereo matching technologies for satellite imagery are based on depth learning. However, the existing depth learning based methods have the problems of holes and matching errors in stereo matching tasks.Methods: In order to improve the effect of satellite image stereo matching results, we propose a satellite image stereo matching network based on attention mechanism (A-SATMVSNet). To solve the problem of insufficient extraction of surface features, a new feature extraction module based on triple dilated convolution with attention module is proposed, which solves the problem of matching holes caused by insufficient extraction of surface features. At the same time, compared with the traditional weighted average method, we design a novel cost-volume method that integrates attention mechanism to reduce the impact of matching errors to improve the accuracy of matching.Results and discussion: Experiments on public multi-view stereo matching dataset based on satellite imagery demonstrate that the proposed method significantly improves the accuracy and outperforms various previous methods. Our source code is available at https://github.com/MVSer/A-SATMVSNet.

Multi-View Stereo Network Based on Attention Mechanism and Neural Volume Rendering

“…Subsequently, dilated convolutional layers with 3 × 3 kernels are employed to expand the receptive field of the input features, facilitating the exploration of deep-level fine-grained features. To address potential information correlation issues associated with the use of triple dilated convolution, we adopt a strategy similar to that of [23], where feature maps are passed through a residual network structure with sigmoid functions after undergoing dilated convolutional layers with different dilation rates. To create the final feature map, the three fine-grained features are combined and run through a convolutional layer with an attention module.…”

“…Completeness indicates the number of surfaces from the ground truth point cloud that are captured in the reconstructed point cloud within the same world coordinates, computed using Formula (22). Overall is the average of Accuracy and Completeness, calculated as per Formula (23).…”

Highly Applicable Iterative Network for 3-D Reconstruction Based on Multiview Satellite Images

“…This approach reduces projection errors and improves the reconstruction accuracy of satellite images. Subsequently, Lin et al [36] introduced A-SATMVSNet, which integrates the attention mechanism into both the feature extraction and cost aggregation modules, aimed at enhancing matching accuracy.…”