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A common way to ray trace subdivision surfaces is by constructing and traversing spatial hierarchies on top of tessellated input primitives. Unfortunately, tessellating surfaces requires a substantial amount of memory storage, and involves significant construction and memory I/O costs. In this paper, we propose a lazy-build caching scheme to efficiently handle these problems while also exploiting the capabilities of today's many-core architectures. To this end, we lazily tessellate patches only when necessary, and utilize adaptive subdivision to efficiently evaluate the underlying surface representation. The core idea of our approach is a shared lazy evaluation cache, which triggers and maintains the surface tessellation. We combine our caching scheme with SIMD-optimized subdivision primitive evaluation and fast hierarchy construction over the tessellated surface. This allows us to achieve high ray tracing performance in complex scenes, outperforming the state of the art while requiring only a fraction of the memory. In addition, our method stays within a fixed memory budget regardless of the tessellation level, which is essential for many applications such as movie production rendering. Beyond the results of this paper, we have integrated our method into Embree, an open source ray tracing framework, thus making interactive ray tracing of subdivision surfaces publicly available.
We present a novel approach that simulates human vision including visual defects such as glaucoma by temporal composition of human vision in real-time on the GPU. Therefore, we determine eye focus points every time step and adapt the lens accommodation of our virtual eye model accordingly. The focal distance is then used to determine bluriness of observed scene regions; i.e., we compute defocus for all visible pixels. In order to simulate the visual memory we introduce a sharpness field where we integrate defocus values temporally. That allows for memorizing sharply perceived scene points. For visualization, we ray trace the virtual scene environment while incorporating depth of field based on the sharpness field data. Thus, our algorithm facilitates the simulation of human vision mimicing the visual memory. We consider this to be particularly useful for illustration purposes for patients with visual defects such as glaucoma. In order to run our algorithm in real-time we employ massively parallel graphics hardware.