The building planar graph reconstruction, a.k.a. footprint reconstruction, which lies in the domain of computer vision and geoinformatics, has been long afflicted with the challenge of redundant parameters in conventional convolutional models. Therefore, in this paper, we proposed an advanced and adaptive shift architecture, namely the Swap operation, which incorporates non-exponential growth parameters while retaining analogous functionalities to integrate local feature spatial information, resembling a high-dimensional convolution operator. The Swap, cross-channel operation, architecture implements the XOR operation to alternately exchange adjacent or diagonal features, and then blends alternating channels through a 1x1 convolution operation to consolidate information from different channels. The SwapNN architecture, on the other hand, incorporates a group-based parameter-sharing mechanism inspired by the convolutional neural network process and thereby significantly reducing the number of parameters. We validated our proposed approach through experiments on the SpaceNet corpus, a publicly available dataset annotated with 2,001 buildings across the cities of Los Angeles, Las Vegas, and Paris. Our results demonstrate the effectiveness of this innovative architecture in building planar graph reconstruction from 2D building images.

翻译：建筑平面图重建（亦称足迹重建）属于计算机视觉与地理信息学领域，长期以来一直受到传统卷积模型中冗余参数问题的困扰。为此，本文提出了一种先进的自适应移位架构——Swap操作。该架构在保持类似集成局部特征空间信息功能（如同高维卷积算子）的同时，采用了非指数增长参数。Swap跨通道操作架构通过XOR运算交替交换相邻或对角特征，随后利用1x1卷积运算混合交替通道，以整合不同通道的信息。另一方面，SwapNN架构借鉴卷积神经网络的处理过程，引入了基于组的参数共享机制，从而显著减少了参数数量。我们在SpaceNet语料库（一个公开数据集，标注了洛杉矶、拉斯维加斯和巴黎三座城市的2001栋建筑）上通过实验验证了所提方法。结果表明，该创新架构在从二维建筑图像重建建筑平面图方面具有有效性。