점진적 광자 매핑을위한 기울기 계산 기법
Gradient Estimation for Progressive Photon Mapping
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초록
점진적 광자 매핑 방식은 복잡한 전역 조명 효과를 효율적으로렌더링할 수 있다. 그러나 샘플이유한한 경우, 반경 축소비율변수에 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 수있을것이다. 본논문에서는점진적광자매핑방식을통한렌더링과매개변수학습이동시에될수있도록기울기를추정하고추정된 기울기를 유한 차분법을 통해 계산된 기울기와 비교하여 검증한다. 본 논문에서 추정된 기울기는 향후 점진적 광자매핑 방식의 렌더링과 매개변수 추정을 동시에 수행하는 온라인 학습 알고리즘에 적용될 수 있을 것으로 기대된다.
Abstract
Progressive photon mapping is a widely adopted rendering technique that conducts a kernel-density estimation on photons pro- gressively 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 perform- ing 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.
참고문헌 (16)
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