Accurate Real-Time Occlusion for Mixed Reality

Properly handling occlusion between real and virtual objects is an important property for any mixed reality (MR) system. Existing methods have typically required known geometry of the real objects in the scene, either specified manually, or reconstructed using a dense mapping algorithm. This limits the situations in which they can be applied. Modern RGBD cameras are cheap and widely available, but the depth information they provide is typically too noisy and incomplete to use directly to provide quality results. In this paper, a method is proposed which makes use of both the colour and depth information provided by an RGBD camera to provide improved occlusion.

This method, Cost Volume Filtering Occlusion, is capable of running in real time, and can also handle occlusion of virtual objects by dynamic, moving objects - such as the user's hands. The method operates on individual RGBD frames as they arrive, meaning it can function immediately in unknown environments, and respond appropriately to sudden changes. The accuracy of the presented method is quantified using a novel approach capable of comparing the results of algorithms such as this to dense SLAM-based approaches. The proposed approach is shown to be capable of producing superior results to both previous image-based approaches and dense RGBD reconstruction, at lower computational cost.