  Photogrammetric mesh models obtained from aerial oblique images have been
widely used for urban reconstruction. However, the photogrammetric meshes also
suffer from severe texture problems, especially on the road areas due to
occlusion. This paper proposes a structure-aware completion approach to improve
the quality of meshes by removing undesired vehicles on the road seamlessly.
Specifically, the discontinuous texture atlas is first integrated to a
continuous screen space through rendering by the graphics pipeline; the
rendering also records necessary mapping for deintegration to the original
texture atlas after editing. Vehicle regions are masked by a standard object
detection approach, e.g. Faster RCNN. Then, the masked regions are completed
guided by the linear structures and regularities in the road region, which is
implemented based on Patch Match. Finally, the completed rendered image is
deintegrated to the original texture atlas and the triangles for the vehicles
are also flattened for improved meshes. Experimental evaluations and analyses
are conducted against three datasets, which are captured with different sensors
and ground sample distances. The results reveal that the proposed method can
quite realistic meshes after removing the vehicles. The structure-aware
completion approach for road regions outperforms popular image completion
methods and ablation study further confirms the effectiveness of the linear
guidance. It should be noted that the proposed method is also capable to handle
tiled mesh models for large-scale scenes. Dataset and code are available at
vrlab.org.cn/~hanhu/projects/mesh.

Computer Vision and Pattern Recognition