Image Valuation in NeRF-based 3D reconstruction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914173785473024 |
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| author | Cheimariotis, Grigorios Aris Karakottas, Antonis Chatzis, Vangelis Kanlis, Angelos Zarpalas, Dimitrios |
| author_facet | Cheimariotis, Grigorios Aris Karakottas, Antonis Chatzis, Vangelis Kanlis, Angelos Zarpalas, Dimitrios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_23052 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Image Valuation in NeRF-based 3D reconstruction Cheimariotis, Grigorios Aris Karakottas, Antonis Chatzis, Vangelis Kanlis, Angelos Zarpalas, Dimitrios Computer Vision and Pattern Recognition I.4.5; I.3.7 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. |
| title | Image Valuation in NeRF-based 3D reconstruction |
| topic | Computer Vision and Pattern Recognition I.4.5; I.3.7 |
| url | https://arxiv.org/abs/2511.23052 |