Gradient-domain path tracing

Kettunen, Markus; Manzi, Marco; Aittala, Miika; Lehtinen, Jaakko; Durand, Frédo; Zwicker, Matthias

doi:10.1145/2766997

Cited by 82 publications

(73 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The seminal work on gradient‐domain path tracing from Kettunen et al [KMA*15] showed that gradient sampling can also lead to significant improvements for standard Monte‐Carlo path tracing. They further proposed a simpler shift mapping which only requires local path information and can be computed sequentially during the path sampling.…”

Section: Related Workmentioning

confidence: 99%

Spectral Gradient Sampling for Path Tracing

Petitjean

Bauszat

Eisemann

2018

Computer Graphics Forum

View full text Add to dashboard Cite

Spectral Monte‐Carlo methods are currently the most powerful techniques for simulating light transport with wavelength‐dependent phenomena (e.g., dispersion, colored particle scattering, or diffraction gratings). Compared to trichromatic rendering, sampling the spectral domain requires significantly more samples for noise‐free images. Inspired by gradient‐domain rendering, which estimates image gradients, we propose spectral gradient sampling to estimate the gradients of the spectral distribution inside a pixel. These gradients can be sampled with a significantly lower variance by carefully correlating the path samples of a pixel in the spectral domain, and we introduce a mapping function that shifts paths with wavelength‐dependent interactions. We compute the result of each pixel by integrating the estimated gradients over the spectral domain using a one‐dimensional screened Poisson reconstruction. Our method improves convergence and reduces chromatic noise from spectral sampling, as demonstrated by our implementation within a conventional path tracer.

show abstract

Section: Related Workmentioning

confidence: 99%

Spectral Gradient Sampling for Path Tracing

Petitjean

Bauszat

Eisemann

2018

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…Numerous variants exist, e.g., sampling in primary space (Kelemen et al, 2002) or in path space (Veach, 1998), as well as modern extensions such as energy redistribution path tracing (ERPT, Cline et al, 2005), manifold exploration (Jakob and Marschner, 2012), half-vector space transport (Kaplanyan et al, 2014), and gradient domain methods (Kettunen et al, 2015).…”

Section: Metropolis Light Transport (Mlt)mentioning

confidence: 99%

“…One notable exception is the method introduced by Korndörfer (2011). It is also based on Voronoi partitioning, but uses a 3-D voxel grid and puts emphasis on achieving a regular quad meshing of the boundary surface.…”

Section: Revised Generation Of Synthetic Glass Shardsmentioning

confidence: 99%

“…The method already supports random noise edge weights in order to generate irregular, i.e., plausible, surface structures, making it a suitable candidate for our task of generating virtual glass shards. Figure 14 shows a scene of the shards generated with the method of Korndörfer (2011) and rendered using our existing Monte Carlo ray tracing framework supporting global illumination, that is, a physically based simulation of light transport, which inherently accounts for phenomena such as reflections and shadows. We have chosen path tracing even without NEE for our task, as the AOI setup exhibits a very large light source.…”

Section: Revised Generation Of Synthetic Glass Shardsmentioning

confidence: 99%

See 1 more Smart Citation

Physically based computer graphics for realistic image formation to simulate optical measurement systems

Retzlaff

Hanika

Beyerer

et al. 2017

J. Sens. Sens. Syst.

View full text Add to dashboard Cite

Abstract. Physically based image synthesis methods, a research direction in computer graphics (CG), are capable of simulating optical measuring systems in their entirety and thus constitute an interesting approach for the development, simulation, optimization, and validation of such systems. In addition, other CG methods, so-called procedural modeling techniques, can be used to quickly generate large sets of virtual samples and scenes thereof that comprise the same variety as physical testing objects and real scenes (e.g., if digitized sample data are not available or difficult to acquire). Appropriate image synthesis (rendering) techniques result in a realistic image formation for the virtual scenes, considering light sources, material, complex lens systems, and sensor properties, and can be used to evaluate and improve complex measuring systems and automated optical inspection (AOI) systems independent of a physical realization. In this paper, we provide an overview of suitable image synthesis methods and their characteristics, we discuss the challenges for the design and specification of a given measuring situation in order to allow for a reliable simulation and validation, and we describe an image generation pipeline suitable for the evaluation and optimization of measuring and AOI systems.

show abstract

“…Gradient-domain rendering approaches, e.g. [Lehtinen et al 2013;Kettunen et al 2015], sample in the gradient domain to exploit the sparsity of gradients in image space. They use finite differences of the path on the image coordinates, whereas our method uses analytical derivatives on all dimensions.…”

Section: Metropolis Light Transportmentioning

confidence: 99%

Anisotropic Gaussian mutations for metropolis light transport through Hessian-Hamiltonian dynamics

Li¹,

Lehtinen

Ramamoorthi

et al. 2015

ACM Trans. Graph.

Self Cite

View full text Add to dashboard Cite

is rendered by our method in roughly 15 hours. BDPT cannot efficiently sample the sparse contribution function and suffers from severe noise. MMLT tends to get trapped in the hard-to-find features and produces correlated noise. MEMLT specializes in finding difficult static specular paths, but does not consider the anisotropy in the time domain, resulting in ghosting artifacts. Our method can efficiently resolve the hard-to-find SDS paths like the specialized method, and is more general so that it can resolve moving caustic paths in the window by capturing the correlation between the time domain and path space. AbstractThe simulation of light transport in the presence of multi-bounce glossy effects and motion is challenging because the integrand is high dimensional and areas of high-contribution tend to be narrow and hard to sample. We present a Markov Chain Monte Carlo (MCMC) rendering algorithm that extends Metropolis Light Transport by automatically and explicitly adapting to the local shape of the integrand, thereby increasing the acceptance rate. Our algorithm characterizes the local behavior of throughput in path space using its gradient as well as its Hessian. In particular, the Hessian is able to capture the strong anisotropy of the integrand. We obtain the derivatives using automatic differentiation, which makes our solution general and easy to extend to additional sampling dimensions such as time.However, the resulting second order Taylor expansion is not a proper distribution and cannot be used directly for importance sampling. Instead, we use ideas from Hamiltonian Monte-Carlo and simulate the Hamiltonian dynamics in a flipped version of the Taylor expansion where gravity pulls particles towards the high-contribution region. Whereas such methods usually require numerical integration, we show that our quadratic landscape leads to a closed-form anisotropic Gaussian distribution for the final particle positions, and it results in a standard Metropolis-Hastings algorithm. Our method excels at rendering glossy-to-glossy reflections on small and highly curved surfaces. Furthermore, unlike previous work that derives sampling anisotropy with pen and paper and only considers specific effects such as specular BSDFs, we characterize the local shape of throughput through automatic differentiation. This makes our approach very general. In particular, our method is the first MCMC rendering algorithm that is able to resolve the anisotropy in the time dimension and render difficult moving caustics.

show abstract

Gradient-domain path tracing

Cited by 82 publications

References 42 publications

Spectral Gradient Sampling for Path Tracing

Spectral Gradient Sampling for Path Tracing

Physically based computer graphics for realistic image formation to simulate optical measurement systems

Anisotropic Gaussian mutations for metropolis light transport through Hessian-Hamiltonian dynamics

Contact Info

Product

Resources

About