Shadow-Aware Point-Based Neural Radiance Fields for High-Resolution Remote Sensing Novel View Synthesis

Novel view synthesis using neural radiance fields (NeRFs) for remote sensing images is important for various applications. Traditional methods often use implicit representations for modeling, which have slow rendering speeds and cannot directly obtain the structure of the 3D scene. Some studies have introduced explicit representations, such as point clouds and voxels, but this kind of method often produces holes when processing large-scale scenes from remote sensing images. In addition, NeRFs with explicit 3D expression are more susceptible to transient phenomena (shadows and dynamic objects) and even plane holes. In order to address these issues, we propose an improved method for synthesizing new views of remote sensing images based on Point-NeRF. Our main idea focuses on two aspects: filling in the spatial structure and reconstructing ray-marching rendering using shadow information. First, we introduce hole detection, conducting inverse projection to acquire candidate points that are adjusted during training to fill the holes. We also design incremental weights to reduce the probability of pruning the plane points. We introduce a geometrically consistent shadow model based on a point cloud to divide the radiance into albedo and irradiance, allowing the model to predict the albedo of each point, rather than directly predicting the radiance. Intuitively, our proposed method uses a sparse point cloud generated with traditional methods for initialization and then builds the dense radiance field. We evaluate our method on the LEVIRNVS data set, demonstrating its superior performance compared to state-of-the-art methods. Overall, our work provides a promising approach for synthesizing new viewpoints of remote sensing images.