  Street-view image has been widely applied as a crucial mobile mapping data
source. The inpainting of street-view images is a critical step for street-view
image processing, not only for the privacy protection, but also for the urban
environment mapping applications. This paper presents a novel Deep Neural
Network (DNN), multi-scale semantic prior Feature guided image inpainting
Network (MFN) for inpainting street-view images, which generate static
street-view images without moving objects (e.g., pedestrians, vehicles). To
enhance global context understanding, a semantic prior prompter is introduced
to learn rich semantic priors from large pre-trained model. We design the
prompter by stacking multiple Semantic Pyramid Aggregation (SPA) modules,
capturing a broad range of visual feature patterns. A semantic-enhanced image
generator with a decoder is proposed that incorporates a novel cascaded
Learnable Prior Transferring (LPT) module at each scale level. For each decoder
block, an attention transfer mechanism is applied to capture long-term
dependencies, and the semantic prior features are fused with the image features
to restore plausible structure in an adaptive manner. Additionally, a
background-aware data processing scheme is adopted to prevent the generation of
hallucinated objects within holes. Experiments on Apolloscapes and Cityscapes
datasets demonstrate better performance than state-of-the-art methods, with
MAE, and LPIPS showing improvements of about 9.5% and 41.07% respectively.
Visual comparison survey among multi-group person is also conducted to provide
performance evaluation, and the results suggest that the proposed MFN offers a
promising solution for privacy protection and generate more reliable scene for
urban applications with street-view images.
