Augmented Reality (AR) tracking for mobile devices is not realiable in environments where large virtual content, such as an entire virtual building, is to be displayed to the user. This paper presents a study on how multiple on-site markers can be used to better align the virtual scene to the real-world environment to improve large-scale AR experiences. The method consists of detecting QR markers from the video stream, and then updating the position, orientation, and scale of the virtual content in order to minimize the error between the real markers and their corresponding virtual markers. This allows for more accurate tracking and can be used in more complex environments. Three different evaluation methods are proposed. Additionally, experiments were conducted to explore the influence of marker density and marker layout. These experiments show that higher density layouts as well as structured layouts lead to higher accuracy and stability.

Diminished reality (DR) is an extension of augmented reality (AR) in which real objects are concealed, removed, or replaced. State of the art DR implementations are written and evaluated on desktop platforms, and those which are aimed at smartphones use auxiliary data or hardware for image completion. This paper introduces a DR app for Android smartphones which can select, track, and remove objects in an unrestricted environment from live video at interactive speed. The application relies exclusively on RGB frames from the phone's camera as input to the DR pipeline. Multiple algorithms for object selection and tracking, as well as a patch-based inpainting approach were adapted. Selection and tracking of objects was very reliable at interactive speed. Inpainting led to convincing results for planar surfaces and unstructured textures, often delivering acceptable results even in more complicated scenes. Future optimizations are required for real-time performance of DR algorithms even on a flagship smartphone, as the hardware was a constraining factor. The performance of inpainting in particular needs to be improved to achieve acceptable frame rates.

According to the Contrast Sensitivity Function, the contrast of different spatial frequency elements of an image will be seen differently depending on distance. We propose a method for compensating for these differences by acting on an image's frequency representation and manipulating the magnitudes of the individual wave functions composing the image. We provide an open source implementation of the algorithm which can be used to process videos and images in real time.

The appearance of an object or scene is determined by factors like the material, the lights, the geometry, the position of the observer, and the surroundings.
Changes in these factors can be simulated using a projector-camera setup.
Other research focuses on changing the appearance from the perspective of the projector, or on projector compensation for slightly warped planar surfaces.
This paper aims to simulate changes in the scene's appearance by actively manipulating the lighting using a projector-camera setup.
It works on not only planar surfaces, but also on more complex geometries.
This is achieved by first doing a calibration, and then using this to optimize a projection.
This projection is optimized to minimize the difference between how the scene looks when the projection is projected and the desired scene.
For low-resolution projectors, it can do this in a few seconds to half a minute.
For higher resolutions, the calibration time and file size get quite big.
This can be solved in future work using different calibration methods.

Finding good viewpoints for catoptric anamorphoses by hand is hard. However, it should be possible to find the optimal viewpoint using just the specifications of the mirror. This problem is solved by first generating a set of candidate viewpoints using the specifications of the mirror. Then all candidates are ranked based on metrics like visible surface area. The optimal is then easily found.

Artists that create mirror anamorphosis usually relies on the principle of trial-and-error and manual mathematical calculations. they have to draw a deformed image on the surface to get the desired reflected image on the mirror object. A drawing interface to make this kind of art does not exist currently. In this paper, we make a suitable drawing interface that satisfies the needs of artists. We analyzed the common needs of artists and propose methods to address them efficiently. Anyone can easily produce mirror anamorphosis with our tool as long they provide the mapping between surface image and reflection image.

Mirror anamorphosis is characterised by a distorted projection, where the combination of a mirror and a specific viewpoint lets the observer see the undistorted image. The use of this technique and complex perspective in general has gone from being niche to becoming mainstream. Raytracing poses itself as a solution to solving the math for such constructions, as it is capable of delivering accurate geometric calculations and a high degree of visual realism. There is a strong connection between the work needed to construct a mirror anamorphosis, and the computations done in raytracing. Raytracing has been a major topic of research with applications in high-quality image rendering. We propose an algorithm which combines raytracing for mirror anamorphosis with texture mapping. We first generate a set of points based on the desired quality, and triangulate them to a two-dimensional triangle mesh using Delaunay triangulation. Using raytracing, the mesh is projected onto the surface bouncing off the mirror. Surface intersections are recorded in a mapping with its respective texture coordinates. Based on this mesh, we do not have to execute our raytracing algorithm again if the projected image is changed, thus providing us with a significant speedup.

The principle of a mirror anamorphosis relies on distortion caused by a reflective object of a particular shape and the perspective of a viewer looking into the reflective object to look at an image on the surface. The distortion of the reflected image is intended to form a recognizable image, while the image on the surface looks completely different. There are many ways a computer could aid the creation of this form of art by determining how the image is being distorted, but in most cases a regular artist would not know specific geometric details of the scene. In these cases a solution is for the creator to provide any image on the surface and an image of its distortion caused by the scene. The creator can match points between both images to guide the computer into calculating the (estimated) distortion.