Dual Attention-Guided Self-Adaptive Aware Cascade Network for Multi-View Stereo and Stereo Matching

Extensive studies have been conducted on multi-view stereo and stereo matching for 3D reconstruction, whereas relatively few methods have been proposed for a large-scale environment. The difficulty of producing high-resolution depth/disparity maps is one of the main reasons. In this paper, we propose a dual attention-guided self-adaptive aware cascade network (DAscNet) that achieves state-of-the-art results for generating high-resolution depth/disparity maps of complex scenes by introducing a cascade inference strategy using a set of input views. A pyramid cost volume fusion and a self-adaptive cost volume cascade are built upon a dual attention-guided context multi-scale feature extraction encoding geometric, spatial and contextual information at gradually finer scales to achieve robust structural representation for predictions. The dual attention-guided context multi-scale feature extraction is made up of two distinct modules that are both based on the attention mechanism. In the pyramid cost volume fusion, an inter-cost attention aggregation module fuses multiple low-resolution dense cost volumes to achieve a robust structural representation for initial predictions. In the self-adaptive cost volume cascade, a changeable depth/disparity range estimation module is employed to alter the depth/disparity searching range interval of following stage based on the prediction information from the previous stage. This module can drive the network to gradually deal with complicated matching ambiguities and make better the accuracy of depth/disparity searching range interval prediction. Experiments on two publicly available datasets, the Tanks and Temples dataset and the DTU dataset, show that DAscNet outperforms prior work. The effectiveness of our proposed method is also supported by statistics on the accuracy, runtime, and GPU memory of other representative methods.