Improved Stochastic Progressive Photon Mapping with Metropolis Sampling

Chen, Jiating; Wang, Bin; Yong, Jun-Hai

doi:10.1111/j.1467-8659.2011.01979.x

Cited by 24 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This reduces the number of photons that we need to store per pass allowing faster photon gathering without loss of caustic lighting quality. Efficient photon distribution is a common problem for all photon tracing techniques and some recent solutions using adaptive and Metropolis based photon sampling could also be applied to our method [2,8,10]. The photon tracing stage is complete when we have deposited the desired number of caustic photons in the scene.…”

Section: Caustic Evaluationmentioning

confidence: 99%

Mixing Monte Carlo and progressive rendering for improved global illumination

2012

View full text Add to dashboard Cite

In this paper, we seek to eliminate the noise caused by caustic paths during progressive Monte Carlo path tracing. We employ a filtering strategy over path space, handling each subspace using specialized derivations of path tracing and progressive photon mapping. Evaluating diffuse paths with path tracing allows the use of sample stratification over both pixels and the image as a whole, whilst sharp detailed caustics are produced using progressive photon mapping. This is an efficient, low noise progressive algorithm with vanishing bias combining the advantages of both Monte Carlo methods, and particle tracing.

show abstract

Section: Caustic Evaluationmentioning

confidence: 99%

Mixing Monte Carlo and progressive rendering for improved global illumination

2012

View full text Add to dashboard Cite

show abstract

“…But the light paths are sampled via path tracing, which is inefficient to sample SDS paths. Chen et al [7] give an importance function based on the initial photon density. But they use the mutation kernel proposed in [10], which cannot adapt to different scenes.…”

Section: Related Workmentioning

confidence: 99%

“…But it traces light paths via path tracing, which cannot efficiently sample SDS paths. Chen et al [7] devise an importance function based on initial photon density, increasing photon density in regions where initial photon density is low. Robust adaptive photon tracing method (RAPT) [8] gives an importance function based on photon path visibility, which enables the method to trace photons to visible regions.…”

Section: Introductionmentioning

confidence: 99%

Visual importance-based adaptive photon tracing

Zheng

2015

Vis Comput

View full text Add to dashboard Cite

This paper proposes an adaptive photon tracing approach based on a novel importance function, which combines visual importance and photon path visibility. The generation of photon path is guided by sampling this function to trace more photons to visible and more contributive regions. As a first step, a hierarchy of visual importance maps is constructed. Next, photon paths are produced using a new hybrid mutation strategy, which consists of large mutation and small mutation. The mutation parameter used in small mutation is automatically adjusted using the adaptive Markov chain sampling method. Meanwhile, to find a suitable initial parameter, a mutation parameter initialization method is developed. Experiments show that, compared with previous methods, this approach yields results with better visual quality and smaller numerical error.

show abstract

“…Fan et al [22] uses Metropolis sampling, along with optional user input to suggest useful paths, to improve rendering quality with photon mapping. Similarly, Chen et al [23] apply Metropolis sampling to Stochastic Progressive Photon Mapping [24]. Hachisuka and Jensen [25] use a simple form of replica exchange to efficiently sample light paths, including outdoor to indoor scenes.…”

Section: Rendering Of Interior Scenesmentioning

confidence: 99%

Importance Driven Environment Map Sampling

Bashford-Rogers

Debattista

Chalmers

2014

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Abstract-In this paper we present an efficient method for supporting Image Based Lighting (IBL) for bidirectional methods. This improves both sampling of the environment, and the detection and sampling of important regions of the scene, such as windows and doors. These parts of the scene often have a small area proportional to that of the entire scene, so paths which pass through them are generated with a low probability. The method proposed in this paper improves sampling efficiency, by taking into account view importance, and modifies the lighting distribution to use light transport information from the camera. This method automatically constructs a sampling distribution in locations which are relevant to the camera position, thereby improving sampling of light paths. This approach can be applied to several bidirectional rendering methods, and results are shown for Bidirectional Path Tracing, Metropolis Light Transport and Progressive Photon Mapping. When compared to other methods, efficiency results demonstrate speed ups of orders of magnitude.

show abstract

Improved Stochastic Progressive Photon Mapping with Metropolis Sampling

Cited by 24 publications

References 28 publications

Mixing Monte Carlo and progressive rendering for improved global illumination

Mixing Monte Carlo and progressive rendering for improved global illumination

Visual importance-based adaptive photon tracing

Importance Driven Environment Map Sampling

Contact Info

Product

Resources

About