用误差峰值可视化引导用户补拍视角，提升移动端新视图合成效率。
User-in-the-Loop View Sampling with Error Peaking Visualization
- 基于局部重建光场，通过误差峰值提示应补拍的视角。
- 仅需少量采样即达满意效果，减少用户认知负荷。
- 适合移动端新视图合成及大场景辐射场重建应用。
增强现实（AR）为新视图合成中的缺失视角样本提供了可视化方案。现有方法要求用户通过3D标注对齐AR显示来拍摄图像，该过程心理负担重，且受制于有限的预设采样区域。为摆脱3D标注和场景探索限制，本文提出利用局部重建的光场并可视化待修复的误差区域，指导用户插入新视角。实验表明，误差峰值可视化更少侵入、降低最终结果的失望感，且在移动设备上的新视图合成系统中，用更少采样即可达到满意效果。此外，该方法亦适用于更大场景的辐射场重建，如3D Gaussian splatting。
原文摘要 · Abstract (English)
Augmented reality (AR) provides ways to visualize missing view samples for novel view synthesis. Existing approaches present 3D annotations for new view samples and task users with taking images by aligning the AR display. This data collection task is known to be mentally demanding and limits capture areas to pre-defined small areas due to the ideal but restrictive underlying sampling theory. To free users from 3D annotations and limited scene exploration, we propose using locally reconstructed light fields and visualizing errors to be removed by inserting new views. Our results show that the error-peaking visualization is less invasive, reduces disappointment in final results, and is satisfactory with fewer view samples in our mobile view synthesis system. We also show that our approach can contribute to recent radiance field reconstruction for larger scenes, such as 3D Gaussian splatting.
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