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Submission data

Full name	An attention-based and deep sparse priori cascade multi-view stereo network
Description	In this study, we aim to improve feature matching correlation, aggregate global contextual information, and enhance the robustness of depth estimation to improve the quality of the reconstruction. We propose an attention-based deep sparse priori cascade multi-view stereo network, ADS-MVSNet. Firstly, we propose a feature extraction module based on the attention mechanism to obtain the regions of interest in the input scene. Secondly, we propose a depth sparse prior strategy module to estimate the depth map of the input scene more accurately. It is followed by refinement of the initial depth map using a coarse-to-fine method to improve the accuracy of point cloud reconstruction.
Programming language(s)	python
Hardware	Intel Xeon CPU E5-2697, 32GB RAM, NVIDIA GTX 1080 8GB
Submission creation date	22 May, 2023
Last edited	23 Jun, 2023

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