Novel Segmentation Metrics for Use in Augmented Reality Advertisement Integration
Julian True, Naimul Khan · 2019
A major application area for Augmented Reality (AR) is advertisement. To achieve easy advertisement integration and management for stakeholders, a quantitative semantic understanding of the world, that adheres to human perception is necessary. Current deep learning-based segmentation algorithms such as Mask R-CNN provide a mask of the real world (e.g. building facades) in a greedy manner, which, although quantitatively accurate, does not adhere to the needs of the AR advertisers for appropriate ad placement, such as ad size, mask continuity, etc. In this paper, we propose three intuitive metrics for evaluating building facade segmentation specifically for advertisement integration, namely, average discontinuity, normalized continuous area, and resolution ratio. Each of these metrics is inspired from the way advertisers may want to view segmentation results within an AR world editor, or the way users may want to experience AR advertisements. Experimental results on a test segmentation shows the importance of such intuitive metrics, where we show how an accurate placement area for a sample 2D advertisement can be easily isolated from a segmented facade using our metrics.