Optimised Path Space Regularisation

Weier, Philippe; Droske, Marc; Hanika, Johannes; Weidlich, Andrea; Vorba, Jiří

doi:10.1111/cgf.14347

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…In practice, the pure‐specular interaction is relaxed by slightly increasing roughness and employing small area light sources. The specular paths can also be selectively mollified and sampled at the cost of introducing bias [KD13, WDH*21].…”

Section: Related Workmentioning

confidence: 99%

Neural Path Sampling for Rendering Pure Specular Light Transport

Yu,

Dong,

Kong

et al. 2023

Computer Graphics Forum

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Multi‐bounce, pure specular light paths produce complex lighting effects, such as caustics and sparkle highlights, which are challenging to render due to their sparse and diverse nature. We introduce a learning‐based method for the efficient rendering of pure specular light transport. The key idea is training a neural network to model the distribution of all specular light paths between pairs of endpoints for one specular object. To achieve this, for each object, our method models the distribution of sparse and diverse specular light paths between two endpoints using smooth 2D maps of ray directions from one endpoint and represents these maps with a 2D convolutional network. We design a training scheme to efficiently sample specular light paths from the scene and train the network. Once trained, our method predicts specular light paths for a given pair of endpoints using the network and employs root‐finding‐based algorithms for rendering the specular light transport. Experimental results demonstrate that our method generates high‐quality results, supports dynamic lighting and moving objects within the scene, and significantly enhances the rendering speed of existing techniques.

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Section: Related Workmentioning

confidence: 99%

Neural Path Sampling for Rendering Pure Specular Light Transport

Yu,

Dong,

Kong

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

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“…Biased methods, such as photon mapping [Hachisuka and Jensen 2009;Jensen and Christensen 1995] and regularization [Jendersie and Grosch 2019;Kaplanyan and Dachsbacher 2013b;Weier et al 2021], are also designed to find difficult paths for caustics. Due to their biased nature, these methods tend to lose details from highfrequency caustics.…”

Section: Related Workmentioning

confidence: 99%

Manifold Path Guiding for Importance Sampling Specular Chains

Fan,

Hong,

Guo

et al. 2023

ACM Trans. Graph.

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Complex visual effects such as caustics are often produced by light paths containing multiple consecutive specular vertices (dubbed specular chains) , which pose a challenge to unbiased estimation in Monte Carlo rendering. In this work, we study the light transport behavior within a sub-path that is comprised of a specular chain and two non-specular separators. We show that the specular manifolds formed by all the sub-paths could be exploited to provide coherence among sub-paths. By reconstructing continuous energy distributions from historical and coherent sub-paths, seed chains can be generated in the context of importance sampling and converge to admissible chains through manifold walks. We verify that importance sampling the seed chain in the continuous space reaches the goal of importance sampling the discrete admissible specular chain. Based on these observations and theoretical analyses, a progressive pipeline, manifold path guiding , is designed and implemented to importance sample challenging paths featuring long specular chains. To our best knowledge, this is the first general framework for importance sampling discrete specular chains in regular Monte Carlo rendering. Extensive experiments demonstrate that our method outperforms state-of-the-art unbiased solutions with up to 40 × variance reduction, especially in typical scenes containing long specular chains and complex visibility.

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“…A possible solution is to connect connections (during learning) using path space regularization [KD13]. Previous work showed how this can benefit path guiding [WDH∗21] by discovering features more easily.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Path Guiding with Vertex Triplet Distributions

Schüßler

Hanika

Jung

et al. 2022

Computer Graphics Forum

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Good importance sampling strategies are decisive for the quality and robustness of photorealistic image synthesis with Monte Carlo integration. Path guiding approaches use transport paths sampled by an existing base sampler to build and refine a guiding distribution. This distribution then guides subsequent paths in regions that are otherwise hard to sample. We observe that all terms in the measurement contribution function sampled during path construction depend on at most three consecutive path vertices. We thus propose to build a 9D guiding distribution over vertex triplets that adapts to the full measurement contribution with a 9D Gaussian mixture model (GMM). For incremental path sampling, we query the model for the last two vertices of a path prefix, resulting in a 3D conditional distribution with which we sample the next vertex along the path. To make this approach scalable, we partition the scene with an octree and learn a local GMM for each leaf separately. In a learning phase, we sample paths using the current guiding distribution and collect triplets of path vertices. We resample these triplets online and keep only a fixed-size subset in reservoirs. After each progression, we obtain new GMMs from triplet samples by an initial hard clustering followed by expectation maximization. Since we model 3D vertex positions, our guiding distribution naturally extends to participating media. In addition, the symmetry in the GMM allows us to query it for paths constructed by a light tracer. Therefore our method can guide both a path tracer and light tracer from a jointly learned guiding distribution. CCS Concepts• Computing methodologies → Ray tracing;

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Optimised Path Space Regularisation

Cited by 4 publications

References 30 publications

Neural Path Sampling for Rendering Pure Specular Light Transport

Neural Path Sampling for Rendering Pure Specular Light Transport

Manifold Path Guiding for Importance Sampling Specular Chains

Path Guiding with Vertex Triplet Distributions

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