The ability to synthesize realistic and diverse indoor furniture layouts
automatically or based on partial input, unlocks many applications, from better
interactive 3D tools to data synthesis for training and simulation. In this
paper, we present ATISS, a novel autoregressive transformer architecture for
creating diverse and plausible synthetic indoor environments, given only the
room type and its floor plan. In contrast to prior work, which poses scene
synthesis as sequence generation, our model generates rooms as unordered sets
of objects. We argue that this formulation is more natural, as it makes ATISS
generally useful beyond fully automatic room layout synthesis. For example, the
same trained model can be used in interactive applications for general scene
completion, partial room re-arrangement with any objects specified by the user,
as well as object suggestions for any partial room. To enable this, our model
leverages the permutation equivariance of the transformer when conditioning on
the partial scene, and is trained to be permutation-invariant across object
orderings. Our model is trained end-to-end as an autoregressive generative
model using only labeled 3D bounding boxes as supervision. Evaluations on four
room types in the 3D-FRONT dataset demonstrate that our model consistently
generates plausible room layouts that are more realistic than existing methods.
In addition, it has fewer parameters, is simpler to implement and train and
runs up to 8 times faster than existing methods.

本文提出了一种新颖的自回归变压器架构 ATISS，用于根据房间类型和地板平面自动生成多样而可信的合成室内环境，并通过 3D-FRONT 数据集的实验表明，该模型比现有方法更为逼真，且参数更少、实现和训练更简单，速度更快。