Year: 2024
Type: article
Abstract: Building facade completion is an important part of digitizing the structures of buildings using computer technology. Due to the intricate textures and structures in building facade images, existing image-completion algorithms cannot accurately restore the rich texture and detailed information. In response, this paper proposes a novel network to simultaneously recover the texture and semantic structural features of building facades. By incorporating dynamic convolutions into each layer of the feature encoder, the shallow layers of the completion network can create a global receptive field, thus enhancing the model’s feature-extraction capability. Additionally, a spatial attention branch is integrated into the dynamic convolution module to boost the correlation between the completion area and its surrounding edge area, resulting in improved edge clarity and accuracy of the completed facade image. Experimental results on multiple public image datasets demonstrate that the proposed model in this paper achieves state-of-the-art results when applied to real-world datasets.
Source: Electronics
Authors: Zhenhuang Cai, Yangbin Lin, Xingwang Huang, Zongliang Zhang, Zongyue Wang
Institution: Jimei University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Award: G1327749636
OpenAlex ID: W4402006551