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).
翻译:由于性能原因,将路径追踪限制在每像素的少量路径上,很少能达到令人感兴趣的场景满意的图像质量。 但是,路径空间过滤可以通过在被归类为近距离路径的顶端共享信息而极大地改善视觉质量。 与屏幕空间方法不同, 这些路径不需要出现在屏幕上, 也不局限于与场景的第一个交叉点。 虽然搜索临近的顶端比在屏幕空间过滤要贵得多, 但是我们通过储存、 更新和在散列桌上搜索所需的信息大大改进了这一性能处罚。 密钥是用快速和量化的信息构建的, 只有单项查询才可能取代昂贵的邻里搜索 。 在图形处理器( GPU) 上展示了对算法的大规模平行实施 。