论文标题
通过结构幻觉变压器级联的平面图修复
Floorplan Restoration by Structure Hallucinating Transformer Cascades
论文作者
论文摘要
本文介绍了极端的平面图重建任务，该任务的新基准和神经体系结构作为解决方案。给定从全景图像推断或策划的部分平面图重建，其任务是重建一个完整的平面图，包括无形的建筑结构。提出的神经网络1）通过卷积神经网络和变压器对输入部分平面图编码为一组潜在媒介； 2）重建整个平面图，同时通过级联变压器解码器来幻觉不可见的房间和门。定性和定量评估表明，我们的方法对701座房屋的基准有效，表现优于最先进的重建技术。我们将共享我们的代码，模型和数据。
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.
