STs-NeRF: Novel View Synthesis of Space Targets Based on Improved Neural Radiance Fields

Abstract

Since Neural Radiation Field (NeRF) was first proposed, a large number of studies dedicated to them have emerged. These fields achieved very good results in their respective contexts, but they are not sufficiently practical for our project. If we want to obtain novel images of satellites photographed in space by another satellite, we must face problems like inaccurate camera focal lengths and poor image texture. There are also some small structures on satellites that NeRF-like algorithms cannot render well. In these cases, the NeRF’s performance cannot sufficiently meet the project’s needs. In fact, the images rendered by the NeRF will have many incomplete structures, while the MipNeRF will blur the edges of the structures on the satellite and obtain unrealistic colors. In response to these problems, we proposed STs-NeRF, which improves the quality of the new perspective images through an encoding module and a new network structure. We found a method for calculating poses that are suitable for our dataset and that enhance the network’s input learning effect by recoding the sampling points and viewing directions through a dynamic encoding (DE) module. Then, we input them into our layer-by-layer normalized multi-layer perceptron (LLNMLP). By simultaneously inputting points and directions into the network, we avoid the mutual influence between light rays, and through layer-by-layer normalization, we ease the model’s overfitting from a training perspective. Since real images should not be made public, we created a synthetic dataset and conducted a series of experiments. The experiments showed that our method achieves the best results in reconstructing captured satellite images, compared with the NeRF, the MipNeRF, the NeuS and the NeRF2Mesh, and improves the Peak Signal-to-Noise Ratio (PSNR) by 19%. We have also tested on public datasets, and our NeRF can still render acceptable images on datasets with better textures.