用跨视图图像质量评估，快速精准选最优视角
Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Assessment
- 将视角选择转为2D图像质量评估，避免复杂3D建模
- 在标准数据集上提升合成质量，速度比之前快14-33倍
- 不依赖具体3D表示，适合各类三维重建任务
我们针对新视角合成与3D重建中的主动视角选择问题提出新方法。现有方法如FisheRF和ActiveNeRF通过最小化不确定性或最大化信息增益来选择下一最佳视角，但需针对不同3D表示设计专用结构，且在3D空间中建模复杂。本文将其重构为2D图像质量评估（IQA）任务，选择当前渲染质量最低的视角。由于候选视角无真实图像，无法使用全参考指标如PSNR、SSIM；而无参考指标如MUSIQ、MANIQA又缺乏多视角上下文。受最近跨参考质量评估框架CrossScore启发，我们训练模型预测多视角场景下的SSIM，用于指导视角选择。所提跨参考IQA框架在标准基准上实现显著的定量与定性提升，对3D表示无偏，运行速度比先前方法快14-33倍。
原文摘要 · Abstract (English)
We tackle active view selection in novel view synthesis and 3D reconstruction. Existing methods like FisheRF and ActiveNeRF select the next best view by minimizing uncertainty or maximizing information gain in 3D, but they require specialized designs for different 3D representations and involve complex modelling in 3D space. Instead, we reframe this as a 2D image quality assessment (IQA) task, selecting views where current renderings have the lowest quality. Since ground-truth images for candidate views are unavailable, full-reference metrics like PSNR and SSIM are inapplicable, while no-reference metrics, such as MUSIQ and MANIQA, lack the essential multi-view context. Inspired by a recent cross-referencing quality framework CrossScore, we train a model to predict SSIM within a multi-view setup and use it to guide view selection. Our cross-reference IQA framework achieves substantial quantitative and qualitative improvements across standard benchmarks, while being agnostic to 3D representations, and runs 14-33 times faster than previous methods.
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