Many lighting methods used in computer graphics such as indirect illumination can have very high computational costs and need to be approximated for real-time applications. These costs can be reduced by means of upsampling techniques which tend to introduce artifacts and affect the visual quality of the rendered image. This paper suggests a versatile approach for accelerating the rendering of screen space methods while maintaining the visual quality. This is achieved by exploiting the low frequency nature of many of these illumination methods and the geometrical continuity of the scene. First the screen space is dynamically divided into separate sub-images, then the illumination is rendered for each sub-image in an adequate resolution and finally the sub-images are put together in order to compose the final image. Therefore we identify edges in the scene and generate masks precisely specifying which part of the image is included in which sub-image. The masks therefore determine which part of the image is rendered in which resolution. A step wise upsampling and merging process then allows optically soft transitions between the different resolution levels. For this paper, the introduced multi-resolution rendering method was implemented and tested on three commonly used lighting methods. These are screen space ambient occlusion, soft shadow mapping and screen space global illumination.
Screen Space Ambient Occlusion (SSAO) is a real-time approximation of the occlusion of ambient light by local geometry. The technique was first presented by Mittring @cite_1 and further developed and improved (e.g. by @cite_21 ).
@cite_21 @cite_1
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Physically plausible illumination at real-time framerates is often achieved using approximations. One popular example is ambient occlusion (AO), for which very simple and efficient implementations are used extensively in production. Recent methods approximate AO between nearby geometry in screen space (SSAO). The key observation described in this paper is, that screen-space occlusion methods can be used to compute many more types of effects than just occlusion, such as directional shadows and indirect color bleeding. The proposed generalization has only a small overhead compared to classic SSAO, approximates direct and one-bounce light transport in screen space, can be combined with other methods that simulate transport for macro structures and is visually equivalent to SSAO in the worst case without introducing new artifacts. Since our method works in screen space, it does not depend on the geometric complexity. Plausible directional occlusion and indirect lighting effects can be displayed for large and fully dynamic scenes at real-time frame rates. Ambient occlusion is a lighting model that approximates the amount of light reaching a point on a diffuse surface based on its directly visible occluders. It gives perceptual clues of curvature and spatial proximity. Like [Mittring 2007] and [Shanmugam and Arikan 2007], we propose a real-time ambient occlusion computation as a postprocessing pass mainly based on a depth image from the eye’s point of view. This approach requires no scenedependent precomputations and is applicable to dynamic scenes. Our proposed method does not have the overocclusion issue from [Shanmugam and Arikan 2007] and samples inside the radius of influence, unlike [Mittring 2007]. This paper presents an online detection-based two-stage multi-object tracking method in dense visual surveillances scenarios with a single camera. In the local stage, a particle filter with observer selection that could deal with partial object occlusion is used to generate a set of reliable tracklets. In the global stage, the detection responses are collected from a temporal sliding window to deal with ambiguity caused by full object occlusion to generate a set of potential tracklets. The reliable tracklets generated in the local stage and the potential tracklets generated within the temporal sliding window are associated by Hungarian algorithm on a modified pairwise tracklets association cost matrix to get the global optimal association. This method is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results prove the effectiveness of our method. We tackle the problem of simultaneously detecting occlusions and estimating optical flow. We show that, under standard assumptions of Lambertian reflection and static illumination, the task can be posed as a convex minimization problem. Therefore, the solution, computed using efficient algorithms, is guaranteed to be globally optimal, for any number of independently moving objects, and any number of occlusion layers. We test the proposed algorithm on benchmark datasets, expanded to enable evaluation of occlusion detection performance.