GPRGS: Sparse Input New View Synthesis Based on Probabilistic Modeling and Feature Regularization

When the number of available training views is limited, the small quantity of images results in insufficient generation of Gaussian ellipsoids, leading to an empty Gaussian model. This constraint limits the generation of Gaussian ellipsoids within 3DGS. If the number of Gaussian ellipsoids is too low, the model is prone to overfitting and may learn incorrect scene geometry. To address this challenge, we propose 3DGS based on Gaussian probabilistic modeling and feature regularization (GPRGS). Our method employs Gaussian probabilistic modeling based on Gaussian distribution features, where we capture feature information from images and establish a Gaussian distribution to model the feature probability map. Additionally, feature regularization is introduced to enhance image features and prevent overfitting. Moreover, we introduce scale and densification thresholds and update the multi-scale densification and pruning strategy to avoid filtering out all low-opacity Gaussian points during the pruning process. We conducted evaluations for new view synthesis with both full and sparse inputs on real and synthetic datasets. The results demonstrate that GPRGS is on par with other models. In sparse environments, we achieve a slight advantage, specifically showing an approximately 4% improvement in the PSNR metric across multiple evaluation metrics.