Extracting View-Dependent Depth Maps from a Collection of Images

Stereo correspondence algorithms typically produce a single depth map. In addition to the usual problems of occlusions and textureless regions, such algorithms cannot model the variation in scene or object appearance with respect to the viewing position. In this paper, we propose a new representation that overcomes the appearance variation problem associated with an image sequence. Rather than estimating a single depth map, we associate a depth map with each input image (or a subset of them). Our representation is motivated by applications such as view interpolation and depth-based segmentation for model-building or layer extraction. We describe two approaches to extract such a representation from a sequence of images.The first approach, which is more classical, computes the local depth map associated with each chosen reference frame independently. The novelty of this approach lies in its combination of shiftable windows, temporal selection, and graph cut optimization. The second approach simultaneously optimizes a set of self-consistent depth maps at multiple key-frames. Since multiple depth maps are estimated simultaneously, visibility can be modeled explicitly and disparity consistency imposed across the different depth maps. Results, which include a difficult specular scene example, show the effectiveness of our approach.     

基于GPU的视点相关自适应细分

利用GPU的强大浮点数计算能力和并行处理能力,提出一种完全基于GPU的视点相关自适应细分内核进行快速细分计算的方法.在GPU中,依次实现视点相关的面片细分深度值计算、基于基函数表的细分表面顶点求值、细分表面绘制等核心步骤,无须与CPU端系统内存进行几何数据交换.视点相关的自适应细分准则在表面绘制精度保持不变的情况下,有效地降低了细分表面的细分深度和细分的计算量,在此基础上完全基于GPU的细分框架使得曲面细分具有快速高效的特点.该方法还可以在局部重要细节用较大深度值进行实时自适应细分,以逼近极限曲面.     

大规模地形的LOD生成算法研究

为了实时地绘制大规模地形数据,提出了一种改进的实时连续LOD生成算法.该算法采用分块分层的思想,首先将大规模高程数据进行分块,然后对块中数据按照分辨率的大小分层存储.根据视点位置和网格空间对象误差的关系建立基于四叉树的LOD模型,从而提高了大规模地形的绘制效率.使用该方法描述了太湖流域的地形,取得了良好的绘制效果.     

结合遮挡裁减的视点相关模型简化方法

层次细节模型简化方法和裁减方法是加速三角网格绘制的两种主要方法.通常模型简化方法是同视点相联系起来,它提供了一种随着观察者视点变化而变化模型的层次细节。然而在存在大量遮挡物的场景中将导致很多被遮挡的物体在较高的细节层次绘制。为了克服这一缺点,作者提出了一种能够结合遮挡裁减的层次细节模型方法。实验结果显示本方法具有较好的视觉效果和加速性能。     

距离加权的二次误差测度多分辨率网格简化

为了有效显示复杂的三维物体网格模型,基于边折叠操作与二次误差测度,给出了建立与视点相关的多分辨率模型的网格简化算法.该方法引入了距离因子与三角形形态品质因子:网格顶点到视点的距离因子使得产生了与视点位置相关的符合观察需要的网格;三角形形态品质因子的引入,提高了简化后新生成的三角形的形态品质.同时,在构造候选边队列时,采取了邻域冻结办法,避免了对模型的某个部位过度简化与过大三角形的出现.实验结果表明,在保证效率的前提下,简化速度快,但显示并无明显失真,简化后的三角形形态品质较好.该算法适应于三角形网格模型的简化、优化及建立多分辨率细节模型.