  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 https://github.com/art-programmer/FloorNet.
