This paper presents an extreme floorplan reconstruction task, a new benchmark for the task, and a neural architecture as a solution. Given a partial floorplan reconstruction inferred or curated from panorama images, the task is to reconstruct a complete floorplan including invisible architectural structures. The proposed neural network 1) encodes an input partial floorplan into a set of latent vectors by convolutional neural networks and a Transformer; and 2) reconstructs an entire floorplan while hallucinating invisible rooms and doors by cascading Transformer decoders. Qualitative and quantitative evaluations demonstrate effectiveness of our approach over the benchmark of 701 houses, outperforming the state-of-the-art reconstruction techniques. We will share our code, models, and data.

翻译：本文介绍了极端的楼层规划重建任务、任务的新基准以及神经结构作为解决方案。考虑到从全景图像中推断或整理的部分楼层规划重建,任务在于重建完整的楼层规划,包括无形建筑结构。拟议的神经网络 1 将输入的部分楼层规划编码为由进化神经网络和变异器组成的一组潜在矢量;2 重建整个楼层计划,同时用变形器解码器对隐形房间和门进行幻觉。 定性和定量评估表明,我们的方法超过701所房屋的基准,超过了最新重建技术。 我们将分享我们的代码、模型和数据。