给每张3D重建图定价，识别并剔除低贡献图像以提升质量。
Image Valuation in NeRF-based 3D reconstruction
- 基于PSNR和MSE评估每张图像对3D重建的贡献度。
- 移除低贡献图像后重建质量下降可控，证明方法有效。
- 适合关注3D内容质量优化与数据价值评估的研究者。
数据估值与变现在扩展现实（XR）和数字媒体领域日益重要。在从一组图像重建3D场景时，不同输入对最终结果的贡献不均。神经辐射场（NeRF）通过优化体素辐射场实现高保真3D重建，但真实场景中图像质量参差、存在遮挡和瞬时物体，导致输入实用性差异显著。本文提出一种量化单张图像对NeRF重建贡献的方法，依据重建质量指标（PSNR和MSE）评估。通过在训练中移除低贡献图像并测量重建保真度变化，验证了该方法的有效性。
原文摘要 · Abstract (English)
Data valuation and monetization are becoming increasingly important across domains such as eXtended Reality (XR) and digital media. In the context of 3D scene reconstruction from a set of images -- whether casually or professionally captured -- not all inputs contribute equally to the final output. Neural Radiance Fields (NeRFs) enable photorealistic 3D reconstruction of scenes by optimizing a volumetric radiance field given a set of images. However, in-the-wild scenes often include image captures of varying quality, occlusions, and transient objects, resulting in uneven utility across inputs. In this paper we propose a method to quantify the individual contribution of each image to NeRF-based reconstructions of in-the-wild image sets. Contribution is assessed through reconstruction quality metrics based on PSNR and MSE. We validate our approach by removing low-contributing images during training and measuring the resulting impact on reconstruction fidelity.
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