摘要：In virtual simulation application, it is often necessary to use Open GL to render large-scale 3D static scenes including urban architectures. Each scene unit generally has individual vertex data and texture. For large-scale data set, it is hard to render all scene units simultaneously. We need to render part of the scene separately, which is called the scene partition and culling. In general, we partition the whole scene into different units on the CPU. We present a scheme that optimize the GPU rendering pipeline to cull the large-scale static scene, which will reduce the CPU suspending time and take full advantage of GPU computing advantages to speed up the rendering efficiency.

摘要：In recent years, image-based anti-aliasing techniques have been widely investigated in real-time computer graphics. Compared to traditional routine of anti-aliasing, image-based methods, which combined with deferred shading, can efficiently decouple anti-aliasing step from standard graphics pipeline. However, most of the existing image-based methods consume video memory, bandwidth and computation resources heavily. In this paper, we propose an optimized anti-aliasing method, which can efficiently reduce the multi-sampling rate by a multi-sampling mask. We carefully designed the rendering system to avoid serial execution on branch divergence, by utilizing both modern GPU’s features and latest shader model. Experimental results demonstrate that our method can achieve high-quality synthesized image with real-time performance.