Learning Indoor Layouts from Simple Point-Clouds
This addresses the unavailability of indoor spatial maps for location-based services, though it is incremental as it builds on existing neural network methods.
The paper tackles the problem of automatically generating indoor floor plans from point-clouds captured by smartphones, achieving Intersection-over-Union scores of 0.80-0.90 in experiments.
Reconstructing a layout of indoor spaces has been a crucial part of growing indoor location based services. One of the key challenges in the proliferation of indoor location based services is the unavailability of indoor spatial maps due to the complex nature of capturing an indoor space model (e.g., floor plan) of an existing building. In this paper, we propose a system to automatically generate floor plans that can recognize rooms from the point-clouds obtained through smartphones like Google's Tango. In particular, we propose two approaches - a Recurrent Neural Network based approach using Pointer Network and a Convolutional Neural Network based approach using Mask-RCNN to identify rooms (and thereby floor plans) from point-clouds. Experimental results on different datasets demonstrate approximately 0.80-0.90 Intersection-over-Union scores, which show that our models can effectively identify the rooms and regenerate the shapes of the rooms in heterogeneous environment.