Attention-Aware Multi-View Stereo

Keyang Luo, Tao Guan, Lili Ju, Yuesong Wang, Zhuo Chen, Yawei Luo; The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1590-1599

Abstract


Multi-view stereo is a crucial task in computer vision, that requires accurate and robust photo-consistency among input images for depth estimation. Recent studies have shown that learning-based feature matching and confidence regularization can play a vital role in this task. Nevertheless, how to design good matching confidence volumes as well as effective regularizers for them are still under in-depth study. In this paper, we propose an attention-aware deep neural network "AttMVS" for learning multi-view stereo. In particular, we propose a novel attention-enhanced matching confidence volume, that combines the raw pixel-wise matching confidence from the extracted perceptual features with the contextual information of local scenes, to improve the matching robustness. Furthermore, we develop an attention-guided regularization module, which consists of multilevel ray fusion modules, to hierarchically aggregate and regularize the matching confidence volume into a latent depth probability volume.Experimental results show that our approach achieves the best overall performance on the DTU dataset and the intermediate sequences of Tanks & Temples benchmark over many state-of-the-art MVS algorithms.

Related Material


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[bibtex]
@InProceedings{Luo_2020_CVPR,
author = {Luo, Keyang and Guan, Tao and Ju, Lili and Wang, Yuesong and Chen, Zhuo and Luo, Yawei},
title = {Attention-Aware Multi-View Stereo},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}