ROI-NeRFs：通过 NeRF 组合实现场景内感兴趣对象的高保真可视化
ROI-NeRFs: Hi-Fi Visualization of Objects of Interest within a Scene by NeRFs Composition
AI总结：
本文提出 ROI-NeRFs 框架，通过 Scene NeRF 与多个 ROI NeRF 的分解及射线级组合渲染，在大规模文化遗产场景中高效实现多感兴趣对象的高保真可视化。
摘要：
高效且准确的三维重建对于文化遗产应用至关重要。本研究应对的挑战是使用神经辐射场（Neural Radiance Fields，NeRFs）以高细节层次（level of detail，LOD）可视化大规模场景中的对象。其目标是通过仅关注相关内容的细节，在保持计算效率的同时提高所选对象的视觉保真度。所提出的 ROI-NeRFs 框架将场景划分为一个以中等细节表示整个场景的 Scene NeRF，以及多个聚焦于用户定义感兴趣对象的 ROI NeRF。在分解阶段，一个以对象为中心的相机选择模块会为每次 NeRF 训练自动分组相关相机。在组合阶段，Ray-level Compositional Rendering 技术融合来自 Scene NeRF 和 ROI NeRFs 的信息，从而支持同时进行多对象渲染组合。在两个真实世界数据集（包括一个关于复杂的十八世纪文化遗产房间的数据集）上开展的定量与定性实验表明，该框架相较基线方法具有优越性能，可提升对象区域的 LOD、减少伪影，并且不会显著增加推理时间。
英文摘要：
Efficient and accurate 3D reconstruction is essential for applications in cultural heritage. This study addresses the challenge of visualizing objects within large-scale scenes at a high level of detail (LOD) using Neural Radiance Fields (NeRFs). The aim is to improve the visual fidelity of chosen objects while maintaining the efficiency of the computations by focusing on details only for relevant content. The proposed ROI-NeRFs framework divides the scene into a Scene NeRF, which represents the overall scene at moderate detail, and multiple ROI NeRFs that focus on user-defined objects of interest. An object-focused camera selection module automatically groups relevant cameras for each NeRF training during the decomposition phase. In the composition phase, a Ray-level Compositional Rendering technique combines information from the Scene NeRF and ROI NeRFs, allowing simultaneous multi-object rendering composition. Quantitative and qualitative experiments conducted on two real-world datasets, including one on a complex eighteen's century cultural heritage room, demonstrate superior performance compared to baseline methods, improving LOD for object regions, minimizing artifacts, and without significantly increasing inference time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
