A new refinement criterion for adaptive sampling in path tracing

Monte Carlo based methods such as path tracing are the only to do the physically correct simulations of global illumination. Due to its high capability to exploit acceleration structures and SIMD parallelism of modern processors, path tracing is becoming potential in the sense of real time. Basically path tracing generates images with heavy noise. Adaptive sampling is an interesting way to produce less noisy images. In this paper, we make use of both the intra-pixel information and the inter-pixel information to propose a new refinement criterion for adaptive sampling in path tracing to lower the Monte Carlo noise appeared in the generated image. For the intra-pixel information, we take advantage of the non-extensive Tsallis entropy as a homogeneity measurement of sample values within a pixel. For the inter-pixel information, we develop an innovative inter-pixel coherence measure based on the magnitude of spatial gradient and the impulsiveness of the distribution. Implementation results demonstrate that our novel method can perform significantly better than the previously typical ones.