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Time-critical multiresolution rendering of large complex models

机译:大型复杂模型的时间关键型多分辨率渲染

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摘要

Very large and geometrically complex scenes, exceeding millions of polygons and hundreds of objects, arise naturally in many areas of interactive computer graphics. Time-critical rendering of such scenes requires the ability to trade visual quality with speed. Previous work has shown that this can be done by representing individual scene components as multiresolution triangle meshes, and performing at each frame a convex constrained optimization to choose the mesh resolutions that maximize image quality while meeting timing constraints. In this paper we demonstrate that the nonlinear optimization problem with linear constraints associated to a large class of quality estimation heuristics is efficiently solved using an active-set strategy. By exploiting the problem structure, Lagrange multiplier estimates and equality-constrained problem solutions are computed in linear time. Results show that our algorithms and data structures provide low memory overhead, smooth level-of-detail control, and guarantee, within acceptable limits, a uniform, bounded frame rate even for widely changing viewing conditions. Implementation details are presented along with the results of tests for memory needs, algorithm timing, and efficacy.
机译:在交互式计算机图形的许多领域中,自然会出现非常大且几何复杂的场景,超过数百万个多边形和数百个对象。对此类场景的时间要求严格的渲染需要能够快速地交换视觉质量。先前的工作表明,可以通过将单个场景组件表示为多分辨率三角形网格并在每个帧上执行凸约束优化来选择在满足时序约束的情况下最大化图像质量的网格分辨率来完成此操作。在本文中,我们证明了使用主动集策略可有效解决与线性质量约束相关的非线性优化问题,该线性约束与一类质量估计启发式算法相关。通过利用问题结构,可以在线性时间内计算拉格朗日乘数估计和等式约束问题解。结果表明,我们的算法和数据结构提供了低内存开销,平滑的细节级别控制,并且即使在变化很大的观看条件下,也可以在可接受的范围内保证均匀,有限制的帧速率。与内存需求,算法时序和功效的测试结果一起提供了实现细节。

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