Shape Reconstruction of Object-Level Building From Single Image Based on Implicit Representation Network

Three-dimensional shape reconstruction of the object-level building (SROLB) is one of the essential issues in remote sensing. Especially, utilizing the single remote sensing image (SRSI) to perform shape reconstruction can offer better scalability and transferability, in terms of simplifying input data. Recently, the methods of shape reconstruction based on neural networks have been widely studied. However, most of them generate models with irregular surfaces and few details. Besides, complex background in SRSI leads to a poor generalization of networks and reduces the quality of generated models. To solve the above problems, an implicit representation network (IRNet) is proposed in this letter. IRNet is composed of two parts: 3-D space decoding and feature extraction. First, the signed distance function (SDF) is employed to fit implicit representation better in the decoding module. Moreover, a multistage weight loss function is designed, making the network generating models with flatter surfaces and more details. Then, a channel attention (CA) module is added to the feature extraction network. It reduces the interference of the background in the image effectively and improves the generalization of the network. Finally, our method generates mesh models of the individual buildings. The experimental results show that a better accuracy can be obtained compared with state-of-the-art methods.