单张图像数据集三维重建 NeRF 算法比较评估

F. Condorelli, Maurizio Perticarini
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引用次数: 0

摘要

摘要从单幅图像重建三维场景是计算机视觉领域的一项重大挑战,尤其是在文化遗产数字化的背景下,因为文化遗产数字化的数据集可能有限或质量较差。本文针对这一挑战,研究了最新和最先进的单图像三维重建算法,重点关注文化遗产保护中的应用。研究利用不同的单图像数据集,评估了各种基于人工智能的算法,特别是神经辐射场(NeRF),在从有限的视觉数据重建详细三维模型方面的优势和局限性。研究包括在无法进入或不存在的遗产地等传统摄影测量方法无法解决的情况下进行实验。研究结果表明,基于 NeRF 的方法可以有效地生成适合可视化和度量分析的精确、高分辨率重建。这些结果有助于加深对基于 NeRF 的方法在处理单一图像输入方面的理解,并为物体定位和沉浸式内容生成等现实世界应用提供了启示。
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Comparative Evaluation of NeRF Algorithms on Single Image Dataset for 3D Reconstruction
Abstract. The reconstruction of three-dimensional scenes from a single image represents a significant challenge in computer vision, particularly in the context of cultural heritage digitisation, where datasets may be limited or of poor quality. This paper addresses this challenge by conducting a study of the latest and most advanced algorithms for single-image 3D reconstruction, with a focus on applications in cultural heritage conservation. Exploiting different single-image datasets, the research evaluates the strengths and limitations of various artificial intelligence-based algorithms, in particular Neural Radiance Fields (NeRF), in reconstructing detailed 3D models from limited visual data. The study includes experiments on scenarios such as inaccessible or non-existent heritage sites, where traditional photogrammetric methods fail. The results demonstrate the effectiveness of NeRF-based approaches in producing accurate, high-resolution reconstructions suitable for visualisation and metric analysis. The results contribute to advancing the understanding of NeRF-based approaches in handling single-image inputs and offer insights for real-world applications such as object location and immersive content generation.
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