  We present DeepPerimeter, a deep learning based pipeline for inferring a full
indoor perimeter (i.e. exterior boundary map) from a sequence of posed RGB
images. Our method relies on robust deep methods for depth estimation and wall
segmentation to generate an exterior boundary point cloud, and then uses deep
unsupervised clustering to fit wall planes to obtain a final boundary map of
the room. We demonstrate that DeepPerimeter results in excellent visual and
quantitative performance on the popular ScanNet and FloorNet datasets and works
for room shapes of various complexities as well as in multiroom scenarios. We
also establish important baselines for future work on indoor perimeter
estimation, topics which will become increasingly prevalent as application
areas like augmented reality and robotics become more significant.
