DL-based floorplan generation from noisy point clouds

Abstract

Remote inspections of unknown and hostile environments can be performed by military/police personnel via deployment of sensors and SLAM-based 3D reconstruction techniques. However, the generated point clouds (PCs) cannot be transmitted to coordinators, because of their volume sizes. A common data-reduction solution is to convert the PC-based 3D models into 2D floorplans. In this paper, we propose a system with an end-to-end network for automated floorplan generation from noisy PCs to estimate the main building structures (doors, windows and walls). First, the noisy 3D PC is column filtered to remove irrelevant or noise points. Second, we project the remaining points onto a grid map. Finally, an end-to-end neural network is trained to extract an accurate line-based floorplan from the grid map. Experimental results reveal that the system generates floorplans that accurately represent the main structures of a building. On average, the estimated floorplans reach 0.73 F1 score for the building-layout evaluation, which outperforms the state-of-the-art methods. Furthermore, the model size is reduced by multiple thousands of times on the average.