让神经辐射场同时捕捉场景整体结构与细微细节。
LookCloser: Frequency-aware Radiance Field for Tiny-Detail Scene
- 基于频域分析构建频率感知的渲染机制
- 在多个数据集上实现更优的细节保留与整体结构还原
- 适合需要高保真细节的虚拟现实与影视制作
人类通过多频段信息感知环境。在沉浸式场景中，人们会自然扫描环境以理解整体结构，同时关注吸引注意力的物体细节。然而，现有NeRF框架主要聚焦于建模高频局部视图或低频整体结构，难以兼顾两者。我们提出FA-NeRF，一种新型频率感知的视图合成框架，可在单一NeRF模型中同时捕捉场景整体结构与高清细节。方法包括3D频域量化分析场景频率分布，实现频率感知渲染；引入频率网格加速收敛与查询；设计频率感知特征重加权策略，平衡不同频段特征。大量实验表明，该方法在完整场景建模中显著优于现有方法，同时有效保留细粒度细节。
原文摘要 · Abstract (English)
Humans perceive and comprehend their surroundings through information spanning multiple frequencies. In immersive scenes, people naturally scan their environment to grasp its overall structure while examining fine details of objects that capture their attention. However, current NeRF frameworks primarily focus on modeling either high-frequency local views or the broad structure of scenes with low-frequency information, which is limited to balancing both. We introduce FA-NeRF, a novel frequency-aware framework for view synthesis that simultaneously captures the overall scene structure and high-definition details within a single NeRF model. To achieve this, we propose a 3D frequency quantification method that analyzes the scene's frequency distribution, enabling frequency-aware rendering. Our framework incorporates a frequency grid for fast convergence and querying, a frequency-aware feature re-weighting strategy to balance features across different frequency contents. Extensive experiments show that our method significantly outperforms existing approaches in modeling entire scenes while preserving fine details. Project page: https://coscatter.github.io/LookCloser/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。