Multi-scale Semantic Prior Features Guided Deep Neural Network for Urban Street-view Image

• URL: http://arxiv.org/abs/2405.10504v1
• Date: Fri, 17 May 2024 03:02:18 GMT
• Title: Multi-scale Semantic Prior Features Guided Deep Neural Network for Urban Street-view Image
• Authors: Jianshun Zeng, Wang Li, Yanjie Lv, Shuai Gao, YuChu Qin,
• Abstract summary: This paper presents a novel Deep Neural Network (DNN) for inpainting street-view images. A semantic prior prompter is introduced to learn rich semantic priors from large pre-trained model. Experiments on Apolloscapes and Cityscapes datasets demonstrate better performance than state-of-the-art methods.
• Score: 1.4473649585131072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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