Unbiased Caustics Rendering Guided by Representative Specular Paths

Wang, Beibei; Wang, Beibei; Tu, Changhe; Xu, Kun; Holzschuch, Nicolas; Yan, Ling‐Qi

doi:10.1145/3550469.3555381

Cited by 6 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It works well with point light sources and specular surfaces, but glossy surfaces and area light sources will likely create noisy results. It could be combined with path guiding methods [Li et al 2022] around the representative specular path for these scenes.…”

Section: Limitations and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Interactive Rendering of Caustics using Dimension Reduction for Manifold Next-Event Estimation

Granizo-Hidalgo,

Holzschuch

2024

Proc. ACM Comput. Graph. Interact. Tech.

Self Cite

View full text Add to dashboard Cite

Specular surfaces, like water surfaces, create caustics by focusing the light being refracted or reflected. These caustics are very important for scene realism, but also challenging to render: to compute them, we need to find the exact path connecting two points through a specular reflective or refractive surface. This requires finding the roots of a complicated function on the surface. Manifold-Exploration methods find these roots using the Newton-Raphson method, but this involves computing path derivatives at each step, which can be challenging. We show that these roots lie on a curve on the surface, which reduces the dimensionality of the search. This dimension reduction greatly improves the search, allowing for interactive rendering of caustics. It also makes implementation easier, as we do not need to compute path derivatives.

show abstract

Section: Limitations and Discussionmentioning

confidence: 99%

“…Manifold Next-Event Estimation methods [Hanika et al 2015;Zeltner et al 2020] compute a single specular path and work well for specular or quasi-specular surfaces with point light sources. Li et al [Li et al 2022] compute caustics for glossy materials using path guiding around the path computed for a specular surface.…”

Section: Related Workmentioning

confidence: 99%

Interactive Rendering of Caustics using Dimension Reduction for Manifold Next-Event Estimation

Granizo-Hidalgo,

Holzschuch

2024

Proc. ACM Comput. Graph. Interact. Tech.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our path sampling scheme is designed for root‐finding‐based pure specular light transport rendering; however, the representation could also be beneficial for encoding other sparsely distributed and difficult paths. One direct solution would be combing the idea of representative specular paths [LWT*22] to support rough surfaces. We wish to pursue these future directions and envision a unified path sampling strategy that leverages machine learning models for efficient generic Monte Carlo rendering.…”

Section: Discussionmentioning

confidence: 99%

“…To address this, Li et al. [LWT*22] recently introduced a path guiding scheme based on representative specular paths found by a global solver [WHY20]. Such a guiding scheme is proven efficient on rendering caustics cast by objects with non‐zero roughness.…”

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

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.

show abstract

“…Path Cuts [WHY20] can find multi‐bounce pure specular paths from a point light to a pinhole camera. This work was later extended to handle caustic effects [LWT*22] by using the extended specular paths to estimate the incident radiance distribution and guide path tracing. This approach can handle multi‐bounce reflective caustics, but it is not accurate enough for near‐specular reflectors with very small roughness and does not support refractive surfaces.…”

Section: Related Workmentioning

confidence: 99%

Efficient Caustics Rendering via Spatial and Temporal Path Reuse

Xu,

Wang,

Wang

2023

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

Caustics are complex optical effects caused by the light being concentrated in a small area due to reflection or refraction on surfaces with low roughness, typically under a sharp light source. Rendering caustic effects is challenging for Monte Carlo‐based approaches, due to the difficulties of sampling the specular paths. One effective solution is using the specular manifold to locate these valid specular paths. Unfortunately, it needs many iterations to find these paths, leading to a long rendering time. To address this issue, our key insight is that the specular paths tend to be similar for neighboring shading points. To this end, we propose to reuse the specular paths spatially. More specifically, we generate some specular path samples with a low sample rate and then reuse these specular path samples as the initialization for specular manifold walk among neighboring shading points. In this way, much fewer specular path‐searching iterations are performed, due to the efficient initialization close to the final solution. Furthermore, this reuse strategy can be extended for dynamic scenes in a temporal manner, such as light moving or specular geometry deformation. Our method outperforms current state‐of‐the‐art methods and can handle multiple bounces of light and various scenes.

show abstract

Unbiased Caustics Rendering Guided by Representative Specular Paths

Cited by 6 publications

References 18 publications

Interactive Rendering of Caustics using Dimension Reduction for Manifold Next-Event Estimation

Interactive Rendering of Caustics using Dimension Reduction for Manifold Next-Event Estimation

Neural Path Sampling for Rendering Pure Specular Light Transport

Efficient Caustics Rendering via Spatial and Temporal Path Reuse

Contact Info

Product

Resources

About