Foveated rendering adapts the image synthesis process to the user’s gaze. By exploiting the human visual system’s limitations, in particular in terms of reduced acuity in peripheral vision, it strives to deliver high-quality visual experiences at very reduced computational, storage, and transmission costs. Despite the very substantial progress made in the past decades, the solution landscape is still fragmented, and several research problems remain open. In this work, we present an up-to-date integrative view of the domain from the point of view of the rendering methods employed, discussing general characteristics, commonalities, differences, advantages, and limitations. We cover, in particular, techniques based on adaptive resolution, geometric simplification, shading simplification, chromatic degradation, as well spatio-temporal deterioration. Next, we review the main areas where foveated rendering is already in use today. We finally point out relevant research issues and analyze research trends.

The depth images from RGB-D cameras contain a substantial amount of artifacts such as holes and flickering. Moreover, for fast moving objects in successive frames, we perceive ghosting artifacts on the depth images. Hence, the poor quality of the depth images limits them to be used in various applications. Here, we propose a gradient based spatial and temporal method of depth enhancement (gSMOOTH) using least median of squares, which deals with these artifacts. For each depth pixel over a sequence of frames, we look for invalid or unstable or drastically changed depth values and use our approach to replace those values with stable and more feasible depth values. Our approach removes the ghosting artifacts and flickering, and attenuates the amount of temporal noise significantly in real time. We conduct experiments with our own- and reference datasets and evaluate our method against reference methods. Experimental results show improvements for both static and dynamic scenes.

We propose a new method to fill missing or invalid values in depth images generated from the Kinect depth sensor. To fill the missing depth values, we use a robust least median of squares (LMedS) approach. We apply our method for telepresence environments, where Kinects are used very often for reconstructing the captured scene in 3D. We introduce a modified 1D LMedS approach for efficient traversal of consecutive image frames. Our approach solves the unstable nature of depth values in static scenes that is perceived as flickering. We obtain very good result both for static and moving objects inside a scene.