We address the task of converting a floorplan and a set of associated photos
of a residence into a textured 3D mesh model, a task which we call Plan2Scene.
Our system 1) lifts a floorplan image to a 3D mesh model; 2) synthesizes
surface textures based on the input photos; and 3) infers textures for
unobserved surfaces using a graph neural network architecture. To train and
evaluate our system we create indoor surface texture datasets, and augment a
dataset of floorplans and photos from prior work with rectified surface crops
and additional annotations. Our approach handles the challenge of producing
tileable textures for dominant surfaces such as floors, walls, and ceilings
from a sparse set of unaligned photos that only partially cover the residence.
Qualitative and quantitative evaluations show that our system produces
realistic 3D interior models, outperforming baseline approaches on a suite of
texture quality metrics and as measured by a holistic user study.

该论文介绍了 Plan2Scene 任务，旨在将一个住宅的平面图和一组相关的照片转换成有纹理的三维网格模型，解决了从一部分不对齐的照片中提取主要表面材质的挑战，并通过用户研究得出表现优异的结果。