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<p>Due to its photorealistic rendering quality and precise geometric representation, Neural Radiance Fields (NeRF) has achieved promising progress in novel view synthesis. However, due to the multi-temporal nature of satellite imagery, existing NeRF methods for satellite scenes struggle to achieve high-quality rendering in scenarios with drastic lighting variations, shadow discrepancies, and transient object interference. To address these challenges, this paper proposes a&#xa0;novel NeRF-based method that incorporates physical modeling of scene illumination and filters transient objects via semantic segmentation. Specifically, our approach models scene lighting based on the bidirectional reflectance distribution function (BRDF), enabling more accurate color reconstruction. Additionally, we introduce a&#xa0;semantic segmentation-based method for transient object removal. By generating masks for transient objects, our model effectively distinguishes between dynamic and static components in the scene, thereby eliminating the interference of transient objects during training. Furthermore, we improve the density field in NeRF by incorporating an occupancy field and the secant method to derive more precise digital surface models (DSM). We conducted extensive experiments on the DFC2019 dataset, and the results demonstrate that our method can produce more photo-realistic results for the task of novel view systhesis and digital surface modeling.</p>

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Aero-NeRF Leveraging Transient Masking and Occupancy-Field DSM Reconstruction

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Jingshi Wang,
  • Kai Xie,
  • Hong Ji,
  • Zhi Gao,
  • Yichen Zhang,
  • Guoqing Wang

摘要

Due to its photorealistic rendering quality and precise geometric representation, Neural Radiance Fields (NeRF) has achieved promising progress in novel view synthesis. However, due to the multi-temporal nature of satellite imagery, existing NeRF methods for satellite scenes struggle to achieve high-quality rendering in scenarios with drastic lighting variations, shadow discrepancies, and transient object interference. To address these challenges, this paper proposes a novel NeRF-based method that incorporates physical modeling of scene illumination and filters transient objects via semantic segmentation. Specifically, our approach models scene lighting based on the bidirectional reflectance distribution function (BRDF), enabling more accurate color reconstruction. Additionally, we introduce a semantic segmentation-based method for transient object removal. By generating masks for transient objects, our model effectively distinguishes between dynamic and static components in the scene, thereby eliminating the interference of transient objects during training. Furthermore, we improve the density field in NeRF by incorporating an occupancy field and the secant method to derive more precise digital surface models (DSM). We conducted extensive experiments on the DFC2019 dataset, and the results demonstrate that our method can produce more photo-realistic results for the task of novel view systhesis and digital surface modeling.