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.

本文介绍一项极端平面图重建任务，提出了一种神经架构作为解决方案；研究结果表明，该方案通过使用卷积神经网络和 Transformer 将输入的部分平面图编码为一组潜在向量，并通过级联 Transformer 解码器重建整个平面图，包括看不见的房间和门等隐含的建筑结构；该方法在 701 个房屋的基准测试中比现有的重建技术表现更优，且作者将与公众分享算法代码、模型和数据等信息。

结构幻化变压器级联的极限平面图重构

Extreme Floorplan Reconstruction by Structure-Hallucinating Transformer Cascades

The ultimate goal of this indoor mapping research is to automatically
reconstruct a floorplan simply by walking through a house with a smartphone in
a pocket. This paper tackles this problem by proposing FloorNet, a novel deep
neural architecture. The challenge lies in the processing of RGBD streams
spanning a large 3D space. FloorNet effectively processes the data through
three neural network branches: 1) PointNet with 3D points, exploiting the 3D
information; 2) CNN with a 2D point density image in a top-down view, enhancing
the local spatial reasoning; and 3) CNN with RGB images, utilizing the full
image information. FloorNet exchanges intermediate features across the branches
to exploit the best of all the architectures. We have created a benchmark for
floorplan reconstruction by acquiring RGBD video streams for 155 residential
houses or apartments with Google Tango phones and annotating complete floorplan
information. Our qualitative and quantitative evaluations demonstrate that the
fusion of three branches effectively improves the reconstruction quality. We
hope that the paper together with the benchmark will be an important step
towards solving a challenging vector-graphics reconstruction problem. Code and
data are available at this https URL

本研究旨在通过智能手机，利用深度神经网络技术重建室内地图，提出了线上勘测算法 FloorNet 核心思路，即将 RGBD 流图像数据分别经过三个大的神经网络模块来提取特征并同时考虑 3D 与 2D 空间信息，这种方法能够有效提高室内平面图的重建准确性。