Enhanced feature pyramid for multi-view stereo with adaptive correlation cost volume

Multi-level features are commonly employed in the cascade network, which is currently the dominant framework in multi-view stereo (MVS). However, there is a potential issue that the recent popular multi-level feature extractor network overlooks the significance of fine-grained structure features for coarse depth inferences in MVS task. Discriminative structure features play an important part in matching and are helpful to boost the performance of depth inference. In this work, we propose an effective cascade-structured MVS model named FANet, where an enhanced feature pyramid is built with the intention of predicting reliable initial depth values. Specifically, the features from deep layers are enhanced with affluent spatial structure information in shallow layers by a bottom-up feature enhancement path. For the enhanced topmost features, an attention mechanism is additionally employed to suppress redundant information and select important features for subsequent matching. To ensure the lightweight and optimal performance of the entire model, an efficient module is built to construct a lightweight and effective cost volume, representing viewpoint correspondence reliably, by utilizing the average similarity metric to calculate feature correlations between reference view and source views and then adaptively aggregating them into a unified correlation cost volume. Extensive quantitative and qualitative comparisons on the DTU and Tanks &Temple benchmarks illustrate that the proposed model exhibits better reconstruction quality than state-of-the-art MVS methods.