Pseudo-Viewpoint Regularized 3D Gaussian Splatting For Remote Sensing Few-Shot Novel View Synthesis

Yijing Wang, Xu Tang, Jingjing Ma, Xiangrong Zhang, Cheng Zhu, Fang Liu, Licheng Jiao

Abstract: In remote sensing (RS), Few-Shot Novel View Synthesis (FS-NVS) focuses on creating images of unobserved viewpoints using limited training images. Recently, 3D Gaussian Splatting (3DGS) has drawn scholars’ attention by its increasing rendering speeds and providing an explicit neural representation for 3D scenes. However, 3DGS tends to overfit limited training data. To tackle this challenge, we propose a Pseudoview Regularized 3DGS (PR3DGS) FSNVS method for RS scenarios. Our PR3DGS method introduces a pseudo-views regularization module to discriminate synthetic RS images generated from training- or pseudo-viewpoints. Therefore, our PR3DGS method can effectively mitigate overfitting in seen views and enhance the model’s capability to generate more realistic RS images from novel viewpoints. Besides, the excellent experimental results on the LEVIR-NVS dataset demonstrate the effectiveness of our method in RS FSNVS.