Correction to: Techniques for BRDF evaluation

Nunes, Mislene da Silva; Nascimento, Fernando Melo; Miranda, Gastão Florêncio; Andrade, Beatriz Trinchão

doi:10.1007/s00371-021-02306-z

Cited by 2 publications

(2 citation statements)

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“…To reduce the variance, methods of sampling by significance were proposed, as well as their extension-multiple sampling by significance [3,4]. Multiple significance sampling is used in bidirectional path sampling methods [5,6], adaptive path sampling methods [7], modern path tracing methods for light transport [8], etc.…”

Section: Introductionmentioning

confidence: 99%

The method of multiple sampling by significance for the visualization of functionally defined scenes

Vyatkin

Dolgovesov²

2023

E3S Web of Conf.

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In ray tracing methods, the key point is to choose the direction for the rays. If many rays are needed not everywhere, but only in some parts of the scene, it is reasonable to increase the selection in these places. As a result, computing resources are not wasted where there is no such need. That is, you need to make selections by significance. In this paper, the visualization of functionally defined scenes is considered. A method of multiple sampling by significance is proposed. The method uses weight functions for multiple sampling by significance. The weighting functions minimize the variance of the multiple sample estimation by significance. Weights can be negative, which reduces the variance. In addition, weights allow you to have additional flexibility when developing a sampling method that accelerates calculations. As a result, acceptable weights were obtained when modeling light transfer. The variance was reduced by using weights in the sample. The dependence of the mean square error on the number of samples is given. Highly realistic functionally defined scenes are visualized. The method is implemented using CPU and GPU. Diagrams of the method's performance are given.

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Section: Introductionmentioning

confidence: 99%

The method of multiple sampling by significance for the visualization of functionally defined scenes

Vyatkin

Dolgovesov²

2023

E3S Web of Conf.

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“…BRDF is a physical quantity that characterizes the reflection characteristics of the target surface space. It is widely used in computer graphics [1][2][3][4], remote sensing [5][6][7][8][9], and Lidar [10][11][12]. BRDF models were divided into the analytical model [13][14][15][16][17] and the empirical model [18][19][20][21][22] according to different modeling methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid BBO-Firefly algorithm for parameter optimization of bidirectional reflection distribution function model

Sun,

Li,

Guo

et al. 2023

Preprint

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We designed a bidirectional reflection distribution function (BRDF) measurement system to research the characteristics of space target materials. This system measures the BRDF data of an aluminum plate and gold foil. A hybrid biogeography-based optimization-firefly (BBO-Firefly) algorithm was proposed to optimize the five-parameter BRDF model. To check the performance of the hybrid BBO-Firefly algorithm, we set up a contrast experiment to optimize the BRDF parameters using the BBO algorithm, Firefly algorithm, and hybrid BBO-Firefly algorithm. The experimental results prove that the hybrid BBO-Firefly algorithm surpasses the BBO algorithm and Firefly algorithm in convergence speed and precision in the same situation and has a better optimization effect. Finally, we substitute the optimization results of the hybrid BPO-Firefly algorithm into the BRDF model to analyze the reflection characteristics of an aluminum plate and gold foil.

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Correction to: Techniques for BRDF evaluation

Cited by 2 publications

References 0 publications

The method of multiple sampling by significance for the visualization of functionally defined scenes

The method of multiple sampling by significance for the visualization of functionally defined scenes

Hybrid BBO-Firefly algorithm for parameter optimization of bidirectional reflection distribution function model

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