@inproceedings{
chan2024point,
title={Point Cloud Densification for 3D Gaussian Splatting from Sparse Input Views},
author={Kin-Chung Chan and Jun Xiao and Hana Lebeta Goshu and Kin-man Lam},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=vW5070FoXi}
}
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 restricts its applications in real-world products. In recent years, many researchers have tried to use 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 for improved initialization of 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 promising 3D rendering models on the ScanNet dataset and the LLFF dataset. In particular, compared with the conventional 3DGS method, the PSNR and SSIM performance gains achieved by our method are up to 1.71dB and 0.07, respectively. In addition, the novel view synthesis obtained by our method demonstrates the highest visual quality with fewer distortions.
Point Cloud Densification for 3D Gaussian Splatting from Sparse Input Views
Kin-Chung Chan Jun Xiao Hana Lebeta Goshu Kin-man Lam