DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences
This work addresses indoor boundary estimation for applications in augmented reality and robotics, but it is incremental as it builds on existing deep methods for depth and segmentation.
The paper tackles the problem of estimating full indoor perimeters from posed monocular RGB sequences, presenting DeepPerimeter, a pipeline that uses deep learning for depth estimation, wall segmentation, and clustering to produce boundary maps, achieving excellent visual and quantitative performance on ScanNet and FloorNet datasets.
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.