  This work presents dense stereo reconstruction using high-resolution images
for infrastructure inspections. The state-of-the-art stereo reconstruction
methods, both learning and non-learning ones, consume too much computational
resource on high-resolution data. Recent learning-based methods achieve top
ranks on most benchmarks. However, they suffer from the generalization issue
due to lack of task-specific training data. We propose to use a less resource
demanding non-learning method, guided by a learning-based model, to handle
high-resolution images and achieve accurate stereo reconstruction. The
deep-learning model produces an initial disparity prediction with uncertainty
for each pixel of the down-sampled stereo image pair. The uncertainty serves as
a self-measurement of its generalization ability and the per-pixel searching
range around the initially predicted disparity. The downstream process performs
a modified version of the Semi-Global Block Matching method with the up-sampled
per-pixel searching range. The proposed deep-learning assisted method is
evaluated on the Middlebury dataset and high-resolution stereo images collected
by our customized binocular stereo camera. The combination of learning and
non-learning methods achieves better performance on 12 out of 15 cases of the
Middlebury dataset. In our infrastructure inspection experiments, the average
3D reconstruction error is less than 0.004m.
