Efficient and Accurate Rendering of Complex Light Sources

Kniep, S.; Häring, S.; Magnor, Marcus

doi:10.1111/j.1467-8659.2009.01484.x

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…In some predictive rendering tasks, the light sources may also be very complex (e.g., [Kniep et al 2009]), and a similar splitting into separate evaluation kernels might be warranted. Monte Carlo rendering of participating media (see, e.g., [Raab et al 2008]) is another case where the execution, consisting of short steps in a possibly separate data structure, differs considerably from the rest of the computation, suggesting that a specialized volume marching kernel would be advantageous.…”

Section: Discussionmentioning

confidence: 99%

Megakernels considered harmful

Laine

Karras

Aila

2013

Proceedings of the 5th High-Performance Graphics Conference

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When programming for GPUs, simply porting a large CPU program into an equally large GPU kernel is generally not a good approach. Due to SIMT execution model on GPUs, divergence in control flow carries substantial performance penalties, as does high register usage that lessens the latency-hiding capability that is essential for the high-latency, high-bandwidth memory system of a GPU. In this paper, we implement a path tracer on a GPU using a wavefront formulation, avoiding these pitfalls that can be especially prominent when using materials that are expensive to evaluate. We compare our performance against the traditional megakernel approach, and demonstrate that the wavefront formulation is much better suited for realworld use cases where multiple complex materials are present in the scene.

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Section: Discussionmentioning

confidence: 99%

Megakernels considered harmful

Laine

Karras

Aila

2013

Proceedings of the 5th High-Performance Graphics Conference

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“…However, dense uncompressed lightfield storage is still required by that method. Directional photon mapping has been used for directly visualizing luminaires but requires many photons to be really useful; evaluating the illumination coming from the luminaire requires an expensive lookup [Kniep et al 2009]. Velázquez et al [2015] is probably the most comprehensive previous solution.…”

Section: Related Workmentioning

confidence: 99%

Neural complex luminaires

Zhu

Bai

et al. 2021

ACM Trans. Graph.

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Complex luminaires, such as grand chandeliers, can be extremely costly to render because the light-emitting sources are typically encased in complex refractive geometry, creating difficult light paths that require many samples to evaluate with Monte Carlo approaches. Previous work has attempted to speed up this process, but the methods are either inaccurate, require the storage of very large lightfields, and/or do not fit well into modern path-tracing frameworks. Inspired by the success of deep networks, which can model complex relationships robustly and be evaluated efficiently, we propose to use a machine learning framework to compress a complex luminaire's lightfield into an implicit neural representation. Our approach can easily plug into conventional renderers, as it works with the standard techniques of path tracing and multiple importance sampling (MIS). Our solution is to train three networks to perform the essential operations for evaluating the complex luminaire at a specific point and view direction, importance sampling a point on the luminaire given a shading location, and blending to determine the transparency of luminaire queries to properly composite them with other scene elements. We perform favorably relative to state-of-the-art approaches and render final images that are close to the high-sample-count reference with only a fraction of the computation and storage costs, with no need to store the original luminaire geometry and materials.

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“…with glossy surfaces [HJ11]. Kniep et al [KHM09] considered difficult configurations of directly visible light sources and extended photon mapping with angular smoothing in addition to the spatial density estimation. Our regularization of pure specular materials is similar to this work, however, in contrast, we smooth only a fraction of light paths and only in the angular domain which introduces bias in fewer places and leads to an overall faster convergence.…”

Section: Related Workmentioning

confidence: 99%

Path Space Regularization for Holistic and Robust Light Transport

Kaplanyan

Dachsbacher

2013

Computer Graphics Forum

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We propose a simple yet powerful regularization framework for robust light transport simulation. It builds on top of existing unbiased methods and resorts to a consistent estimation using regularization only for paths which cannot be sampled in an unbiased way. To introduce as little bias as possible, we selectively regularize individual interactions along paths, and also derive the regularization consistency conditions. Our approach is compatible with the majority of unbiased methods, e.g. (bidirectional) path tracing and Metropolis light transport (MLT), and only a simple modification is required to adapt existing renderers. We compare to recent unbiased and consistent methods and show examples of scenes with difficult light paths, where regularization is required to account for all illumination features. When coupled with MLT we are able to sample all phenomena, like recent consistent methods, while achieving superior convergence.

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Efficient and Accurate Rendering of Complex Light Sources

Cited by 8 publications

References 17 publications

Megakernels considered harmful

Megakernels considered harmful

Neural complex luminaires

Path Space Regularization for Holistic and Robust Light Transport

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