用神经辐射场预测卫星影像的季节变化，提升对雪盖、色彩等细节的建模能力。
Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery
- 基于月度嵌入向量扩展Sat-NeRF，显式建模季节变化。
- 在不同月份的影像上验证，模型对雪盖和纹理的还原更准确。
- 适合遥感、地理信息与3D重建方向的研究者参考。
本文研究了将神经辐射场（NeRF）应用于卫星影像时的季节预测能力。聚焦于卫星数据的使用，探讨了一种名为Sat-NeRF的新方法在不同月份间预测季节变化的表现。通过综合分析与可视化，评估了模型捕捉和预测季节性变化的能力，揭示了太阳方位角对预测结果的影响，并展示了在雪盖、颜色准确性及不同地貌纹理表示上的细微表现。基于这些发现，提出Planet-NeRF，一种通过一组月度嵌入向量引入季节变化的扩展方法。对比实验表明，在存在季节变化的情况下，Planet-NeRF优于先前模型。该方法与广泛评估相结合，为本领域未来研究提供了有前景的方向。
原文摘要 · Abstract (English)
In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, performs in predicting seasonal variations across different months. Through comprehensive analysis and visualization, the study examines the model's ability to capture and predict seasonal changes, highlighting specific challenges and strengths. Results showcase the impact of the sun direction on predictions, revealing nuanced details in seasonal transitions, such as snow cover, color accuracy, and texture representation in different landscapes. Given these results, we propose Planet-NeRF, an extension to Sat-NeRF capable of incorporating seasonal variability through a set of month embedding vectors. Comparative evaluations reveal that Planet-NeRF outperforms prior models in the case where seasonal changes are present. The extensive evaluation combined with the proposed method offers promising avenues for future research in this domain.
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