Image Valuation in NeRF-based 3D reconstruction

• URL: http://arxiv.org/abs/2511.23052v1
• Date: Fri, 28 Nov 2025 10:23:13 GMT
• Title: Image Valuation in NeRF-based 3D reconstruction
• Authors: Grigorios Aris Cheimariotis, Antonis Karakottas, Vangelis Chatzis, Angelos Kanlis, Dimitrios Zarpalas,
• Abstract summary: In 3D scene reconstruction, not all inputs contribute equally to the final output.<n>NeRFs enable 3D reconstruction of scenes by optimizing a volumetric radiance field given a set of images.<n>We propose a method to quantify the individual contribution of each image to NeRF-based reconstructions of in-the-wild image sets.
• Score: 2.161889957040211
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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