Object Detection in Floor Plans for Automated VR Environment Generation

Abstract

The development of visually compelling Virtual Reality (VR) environments for serious games is a complex task. Most environments are designed using game engines such as Unity or Unreal Engine and require hours if not days of work. However, most important information of indoor environments can be represented by floor plans. Those have been used in architecture for centuries as a fast and reliable way of depicting building configurations. Therefore, the idea of easing the creation of VR ready environments using floor plans is of great interest. In this paper we propose an automated framework to detect and classify objects in floor plans using a neural network trained with a custom floor plan dataset generator. We evaluate our system on three floor plans datasets: ROBIN (labelled), PFG (our own Procedural Floor plan Generation method) and 100 labelled samples from the CubiCasa Dataset