大规模并行路径空间滤波
Massively Parallel Path Space Filtering
摘要：
出于性能原因，将路径追踪限制为每像素少量路径很少能为感兴趣的场景实现令人满意的图像质量。然而，路径空间过滤可以通过跨被分类为邻近的路径的顶点共享信息来显著地改善视觉质量。与基于屏幕空间的方法不同，这些路径既不需要出现在屏幕上，过滤也不限于与场景的第一个交叉点。虽然搜索邻近顶点比在屏幕空间中过滤更昂贵，但通过在哈希表中存储、更新和查找所需信息，我们大大改善了这种性能损失。这些键是从抖动和量化的信息中构造的，因此只有一个查询很可能取代昂贵的邻域搜索。该算法的大规模并行实现上的图形处理单元（GPU）。
英文摘要：
Restricting path tracing to a small number of paths per pixel for performance reasons rarely achieves a satisfactory image quality for scenes of interest. However, path space filtering may dramatically improve the visual quality by sharing information across vertices of paths classified as proximate. Unlike screen space-based approaches, these paths neither need to be present on the screen, nor is filtering restricted to the first intersection with the scene. While searching proximate vertices had been more expensive than filtering in screen space, we greatly improve over this performance penalty by storing, updating, and looking up the required information in a hash table. The keys are constructed from jittered and quantized information, such that only a single query very likely replaces costly neighborhood searches. A massively parallel implementation of the algorithm is demonstrated on a graphics processing unit (GPU).
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