Vehicle clearance based deep learning for oblique 3D model

doi:10.13474/j.cnki.11-2246.2023.0079

Abstract

Abstract: To solve the texture mapping problem caused by the moved car in oblique 3D model, the vehicle clearance method based on deep learning (VCDL) is proposed in this paper. In the proposed method, the vehicles are detected based on deep-object-detection CNN, obtaining the location of the vehicles. And then the vehicles are removed from the model based on deep image inpainting network and fills road textures automatically. Compared with hand-craft vehicles clearance based on Photoshop, the method proposed in this paper can effectively improve the efficiency.

Key words: oblique 3D model, object-detection, deep image inpainting, vehicle clearance

CLC Number:

P232

DING Lele, LIU Yanfei, MENG Fanxiao, ZHANG Tao, WANG Zhen, PAN Yuming. Vehicle clearance based deep learning for oblique 3D model[J]. Bulletin of Surveying and Mapping, 2023, 0(3): 94-98.

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