Stochastic progressive photon mapping(SPPM)is one of the important global illumination methods in computer graphics.It can simulate caustics and specular-diffuse-specular lighting effects efficiently.However,as a bias...Stochastic progressive photon mapping(SPPM)is one of the important global illumination methods in computer graphics.It can simulate caustics and specular-diffuse-specular lighting effects efficiently.However,as a biased method,it always suffers from both bias and variance with limited iterations,and the bias and the variance bring multi-scale noises into SPPM renderings.Recent learning-based methods have shown great advantages on denoising unbiased Monte Carlo(MC)methods,but have not been leveraged for biased ones.In this paper,we present the first learning-based method specially designed for denoising-biased SPPM renderings.Firstly,to avoid conflicting denoising constraints,the radiance of final images is decomposed into two components:caustic and global.These two components are then denoised separately via a two-network framework.In each network,we employ a novel multi-residual block with two sizes of filters,which significantly improves the model’s capabilities,and makes it more suitable for multi-scale noises on both low-frequency and high-frequency areas.We also present a series of photon-related auxiliary features,to better handle noises while preserving illumination details,especially caustics.Compared with other state-of-the-art learning-based denoising methods that we apply to this problem,our method shows a higher denoising quality,which could efficiently denoise multi-scale noises while keeping sharp illuminations.展开更多
Global illumination is the core part of photo-realistic rendering. The photon mapping algorithm is an effective method for computing global illumination with its obvious advantage of caustic and color bleeding renderi...Global illumination is the core part of photo-realistic rendering. The photon mapping algorithm is an effective method for computing global illumination with its obvious advantage of caustic and color bleeding rendering. It is an active research field that has been developed over the past two decades. The deficiency of precise details and efficient rendering are still the main challenges of photon mapping. This report reviews recent work and classifies it into a set of categories including radiance estimation, photon relaxation, photon tracing, progressive photon mapping, and parallel methods. The goals of our report are giving readers an overall introduction to photon mapping and motivating further research to address the limitations of existing methods.展开更多
In this paper,we present a method for fluid simulation based on smoothed particle hydrodynamic(SPH)with fast collision detection on boundaries on GPU.The major goal of our algorithm is to get a fast SPH simulation and...In this paper,we present a method for fluid simulation based on smoothed particle hydrodynamic(SPH)with fast collision detection on boundaries on GPU.The major goal of our algorithm is to get a fast SPH simulation and rendering on GPU.Additionally,our algorithm has the following three features:At first,to make the SPH method GPU-friendly,we introduce a spatial hash method for neighbor search.After sorting the particles based on their grid index,neighbor search can be done quickly on GPU.Second,we propose a fast particle-boundary collision detection method.By precomputing the distance field of scene boundaries,collision detection’s computing cost arrived as O(n),which is much faster than the traditional way.Third,we propose a pipeline with fine-detail surface reconstruction,and progressive photon mapping working on GPU.We experiment our algorithm on different situations and particle numbers of scenes,and find out that our method gets good results.Our experimental data shows that we can simulate 100K particles,and up to 1000K particles scene at a rate of approximately 2 times per second.展开更多
基金This work was partially supported by the National Key Research and Development Program of China under Grant No.2017YFB0203000the National Natural Science Foundation of China under Grant Nos.61802187,61872223,and 61702311the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20170857.
文摘Stochastic progressive photon mapping(SPPM)is one of the important global illumination methods in computer graphics.It can simulate caustics and specular-diffuse-specular lighting effects efficiently.However,as a biased method,it always suffers from both bias and variance with limited iterations,and the bias and the variance bring multi-scale noises into SPPM renderings.Recent learning-based methods have shown great advantages on denoising unbiased Monte Carlo(MC)methods,but have not been leveraged for biased ones.In this paper,we present the first learning-based method specially designed for denoising-biased SPPM renderings.Firstly,to avoid conflicting denoising constraints,the radiance of final images is decomposed into two components:caustic and global.These two components are then denoised separately via a two-network framework.In each network,we employ a novel multi-residual block with two sizes of filters,which significantly improves the model’s capabilities,and makes it more suitable for multi-scale noises on both low-frequency and high-frequency areas.We also present a series of photon-related auxiliary features,to better handle noises while preserving illumination details,especially caustics.Compared with other state-of-the-art learning-based denoising methods that we apply to this problem,our method shows a higher denoising quality,which could efficiently denoise multi-scale noises while keeping sharp illuminations.
基金Project supported by the National Natural Science Foundation of China(Nos.61472224 and 61472225)the Young Scholars Program of Shandong University,China(No.2015WLJH41)+2 种基金the Shandong Key Research and Development Program,China(No.2015GGX106006)the Special Funding of Independent Innovation and Transformation of Achievements in Shandong Province of China(No.2014ZZCX08201)the Special Funds of Taishan Scholar Construction Project,China
文摘Global illumination is the core part of photo-realistic rendering. The photon mapping algorithm is an effective method for computing global illumination with its obvious advantage of caustic and color bleeding rendering. It is an active research field that has been developed over the past two decades. The deficiency of precise details and efficient rendering are still the main challenges of photon mapping. This report reviews recent work and classifies it into a set of categories including radiance estimation, photon relaxation, photon tracing, progressive photon mapping, and parallel methods. The goals of our report are giving readers an overall introduction to photon mapping and motivating further research to address the limitations of existing methods.
文摘In this paper,we present a method for fluid simulation based on smoothed particle hydrodynamic(SPH)with fast collision detection on boundaries on GPU.The major goal of our algorithm is to get a fast SPH simulation and rendering on GPU.Additionally,our algorithm has the following three features:At first,to make the SPH method GPU-friendly,we introduce a spatial hash method for neighbor search.After sorting the particles based on their grid index,neighbor search can be done quickly on GPU.Second,we propose a fast particle-boundary collision detection method.By precomputing the distance field of scene boundaries,collision detection’s computing cost arrived as O(n),which is much faster than the traditional way.Third,we propose a pipeline with fine-detail surface reconstruction,and progressive photon mapping working on GPU.We experiment our algorithm on different situations and particle numbers of scenes,and find out that our method gets good results.Our experimental data shows that we can simulate 100K particles,and up to 1000K particles scene at a rate of approximately 2 times per second.
