  In this paper, a complete pipeline for image-based 3D reconstruction of urban
scenarios is proposed, based on PatchMatch Multi-View Stereo (MVS). Input
images are firstly fed into an off-the-shelf visual SLAM system to extract
camera poses and sparse keypoints, which are used to initialize PatchMatch
optimization. Then, pixelwise depths and normals are iteratively computed in a
multi-scale framework with a novel depth-normal consistency loss term and a
global refinement algorithm to balance the inherently local nature of
PatchMatch. Finally, a large-scale point cloud is generated by back-projecting
multi-view consistent estimates in 3D. The proposed approach is carefully
evaluated against both classical MVS algorithms and monocular depth networks on
the KITTI dataset, showing state of the art performances.
