Journal of the Korea Computer Graphics Society (한국컴퓨터그래픽스학회논문지)

Korea Computer Graphics Society (한국컴퓨터그래픽스학회)
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Gradient Estimation for Progressive Photon Mapping

점진적 광자 매핑을 위한 기울기 계산 기법

  • Donghee Jeon (Gwangju Institute of Science and Technology) ;
  • Jeongmin Gu (Gwangju Institute of Science and Technology) ;
  • Bochang Moon (Gwangju Institute of Science and Technology)
  • 전동희 (광주과학기술원) ;
  • 구정민 (광주과학기술원) ;
  • 문보창 (광주과학기술원)
  • Received : 2024.06.15
  • Accepted : 2024.07.05
  • Published : 2024.07.25
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Abstract

Progressive photon mapping is a widely adopted rendering technique that conducts a kernel-density estimation on photons progressively generated from lights. Its hyperparameter, which controls the reduction rate of the density estimation, highly affects the quality of its rendering image due to the bias-variance tradeoff of pixel estimates in photon-mapped results. We can minimize the errors of rendered pixel estimates in progressive photon mapping by estimating the optimal parameters based on gradient-based optimization techniques. To this end, we derived the gradients of pixel estimates with respect to the parameters when performing progressive photon mapping and compared our estimated gradients with finite differences to verify estimated gradients. The gradient estimated in this paper can be applied in an online learning algorithm that simultaneously performs progressive photon mapping and parameter optimization in future work.

점진적 광자 매핑 방식은 복잡한 전역 조명 효과를 효율적으로 렌더링할 수 있다. 그러나 샘플이 유한한 경우, 반경 축소비율 변수에 의해 분산과 편향 값이 크게 영향 받는다. 유한한 샘플을 사용한 렌더링 결과의 픽셀 오류 및 기울기를 추정하여 추정된 기울기를 기반으로 반경 축소비율을 결정하는 최적의 매개변수를 학습할 수 있다면, 렌더링 된 이미지의 오류를 줄일 수 있을 것이다. 본 논문에서는 점진적 광자 매핑 방식을 통한 렌더링과 매개변수 학습이 동시에 될 수 있도록 기울기를 추정하고 추정된 기울기를 유한 차분법을 통해 계산된 기울기와 비교하여 검증한다. 본 논문에서 추정된 기울기는 향후 점진적 광자 매핑 방식의 렌더링과 매개변수 추정을 동시에 수행하는 온라인 학습 알고리즘에 적용될 수 있을 것으로 기대된다.

Keywords

Acknowledgement

각 장면을 만드는 데 도움을 주신 저자와 3D 장면 저작자 분들께 감사를 표한다: Toshiya Hachisuka (BOX, TORUS) 및 Benedikt Bitterli (C-BOX, WATER). 이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구이다(No. RS-2023-00207939).

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