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3D Gaussian Modeling and Ray Marching of OpenVDB datasets for Scientific Visualization

1. 연구 배경 및 필요성 OpenVDB 는 Sparse Voxel Grid를 이용해 메모리 효율성을 극대화한 포맷으로, 최근 시뮬레이션·렌더링 분야에서 표준으로 자리 잡았다. 하지만 기존 볼류메트릭 렌더링은 샘플링 기반 접근법에 의존해 고해상도 데이터에서 노이즈와 높은 연산 비용이 문제였다. 가우시안 모델링 은 연속적인 확률 밀도 함수를 제공해, 이산적인 Voxel 데이터를 부드러운 함수 형태로 변환한다. 이는 레이 마칭 시 미분 가능성 과 스무딩 효과 를 제공해, 시각적 품질을 크게 향상시킬 수 있다. 2. 핵심 기법 | 단계

3D Gaussian Modeling and Ray Marching of OpenVDB datasets for Scientific Visualization

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