Revisiting PatchMatch Multi-View Stereo for Urban 3D Reconstruction

• URL: http://arxiv.org/abs/2207.08439v1
• Date: Mon, 18 Jul 2022 08:45:54 GMT
• Title: Revisiting PatchMatch Multi-View Stereo for Urban 3D Reconstruction
• Authors: Marco Orsingher, Paolo Zani, Paolo Medici, Massimo Bertozzi
• Abstract summary: A complete pipeline for image-based 3D reconstruction of urban scenarios is proposed, based on PatchMatch Multi-View Stereo (MVS) The proposed approach is carefully evaluated against both classical MVS algorithms and monocular depth networks on the KITTI dataset.
• Score: 1.1011268090482573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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