Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery

TL;DR

This paper investigates how Neural Radiance Fields can model seasonal changes in satellite imagery, introducing Planet-NeRF to improve predictions of seasonal variability and outperform existing models.

Contribution

The study introduces Planet-NeRF, an extension of Sat-NeRF, that incorporates seasonal variability through month embeddings, enhancing 3D reconstruction accuracy across seasons.

Findings

Planet-NeRF outperforms prior models in seasonal prediction tasks.

Sun direction significantly influences NeRF predictions.

Seasonal features like snow cover and color accuracy are effectively captured.

Abstract

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.