Point Cloud Densification for 3D Gaussian Splatting from Sparse Input Views

The technique of 3D Gaussian splatting (3DGS) has demonstrated its effectiveness and efficiency in rendering photo-realistic images for novel view synthesis. However, 3DGS requires a high density of camera coverage, and its performance inevitably degrades with sparse training views, which significantly limits its applicability in real-world scenarios. In recent years, many researchers have explored the use of depth information to alleviate this problem, but the performance of their methods is sensitive to the accuracy of depth estimation. To this end, we propose an efficient method to enhance the performance of 3DGS with sparse training views. Specifically, instead of applying depth maps for regularization, we propose a densification method that generates high-quality point clouds, providing a superior initialization for 3D Gaussians. Furthermore, we propose Systematically Angle of View Sampling (SAOVS), which employs Spherical Linear Interpolation (SLERP) and linear interpolation for side view sampling, to determine unseen views outside the training data for semantic pseudo-label regularization. Experiments show that our proposed method significantly outperforms other leading 3D rendering models on the ScanNet dataset and the LLFF dataset. In particular, compared with the conventional 3DGS method, our proposed method achieves performance gains of up to 1.71dB in PSNR and 0.07 in SSIM. In addition, the novel view synthesis produced by our method demonstrates the highest visual quality with minimal distortions.