Virtual reality rendering methods for training deep learning, analysing landscapes, and preventing virtual reality sickness

Abstract

Virtual reality (VR) has been proposed for various purposes such as design studies, presentation, simulation and communication in the field of computer-aided architectural design. This paper explores new roles for VR; in particular, we propose rendering methods that consist of post-processing rendering, segmentation rendering and shadow-casting rendering for more-versatile approaches in the use of data. We focus on the creation of a dataset of annotated images, composed of paired foreground-background and semantic-relevant images, in addition to traditional immersive rendering for training deep learning neural networks and analysing landscapes. We also develop a camera velocity rendering method using a customised segmentation rendering technique that calculates the linear and angular velocities of the virtual camera within the VR space at each frame and overlays a colour on the screen according to the velocity value. Using this velocity information, developers of VR applications can improve the animation path within the VR space and prevent VR sickness. We successfully applied the developed methods to urban design and a design project for a building complex. In conclusion, the proposed method was evaluated to be both feasible and effective.