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<p>Ancient buildings are invaluable cultural heritage assets, traditional preservation methods often suffer from inefficiency and low accuracy. We propose FaTNeRF, a NeRF-based framework that integrates adaptive frequency encoding and a novel density loss for high-quality rendering and digital preservation. Evaluations on public datasets show our method achieves fast reconstruction (&lt;20 min) with superior visual quality while maintaining compact model size (&lt;75 MB). Additional validation on a self-collected cultural heritage dataset confirms its effectiveness in complex real-world scenarios. FaTNeRF advances digital preservation by enabling efficient, high-fidelity 3D reconstruction for historic buildings.</p>

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Frequency and tensorial neural radiance fields for digital preservation of ancient buildings

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月12日

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  • Chenxu Li,
  • Dong Sui,
  • Jin Li,
  • Yacong Li,
  • Yang Li,
  • Maozu Guo

摘要

Ancient buildings are invaluable cultural heritage assets, traditional preservation methods often suffer from inefficiency and low accuracy. We propose FaTNeRF, a NeRF-based framework that integrates adaptive frequency encoding and a novel density loss for high-quality rendering and digital preservation. Evaluations on public datasets show our method achieves fast reconstruction (<20 min) with superior visual quality while maintaining compact model size (<75 MB). Additional validation on a self-collected cultural heritage dataset confirms its effectiveness in complex real-world scenarios. FaTNeRF advances digital preservation by enabling efficient, high-fidelity 3D reconstruction for historic buildings.