Attention-Enhanced Multi-View Stereo with Probabilistic Depth Variance Refinement

Sixian Chan, Aofeng Qiu, Wei Pan, Jiafa Mao

Abstract: Multi-view stereo (MVS) reconstruction is a fundamental task in computer vision. While learning-based multi-view stereo methods have demonstrated excellent performance, insufficient attention has been paid to areas with significant depth estimation uncertainty. To address this issue, we propose an attention-enhanced network with probabilistic depth variance refinement for multi-view stereo reconstruction called AE-DR-MVSNet. Specifically, it contains two kernel modules: a diverse attention fusion module(DAFM) and a probability depth variance refinement module(PDVRM). The DAFM is designed by fusing the Bi-Level routing attention and the efficient multi-scale attention to achieve robust multi-scale features. The PDVRM is advanced to enhance depth estimation accuracy and adaptability by dynamically adjusting the depth hypothesis range based on depth distribution variance. Finally, extensive experiments conducted on benchmark datasets, including DTU and Blend-edMVS, demonstrate that AE-DR-MVSNet achieves competitive performance compared to lots of traditional and learning-based methods.