论文标题
重新访问城市3D重建的补丁摩托车多视图立体声
Revisiting PatchMatch Multi-View Stereo for Urban 3D Reconstruction
论文作者
论文摘要
在本文中，根据PatchMatch Multi-View Stereo（MVS），提出了针对城市场景的基于图像的3D重建的完整管道。首先，输入图像被馈入现成的视觉大满贯系统中，以提取相机姿势和稀疏关键点，这些镜头用于初始化PatchMatch优化。然后，在具有新颖的深度正常一致性损耗项和全局细化算法的多尺度框架中对Pixelwise的深度和正态进行迭代计算，以平衡斑块固有的局部性质。最后，通过在3D中以反向项目的多视图一致估计来生成大规模点云。针对Kitti数据集上的经典MVS算法和单眼深度网络仔细评估了所提出的方法，显示了最先进的性能状态。
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.
