Depth-Regularized 3D Gaussian Splatting for Indoor Panoramic images

3D Gaussian Splatting (3DGS) has recently made notable progress in photo-realistic view synthesis and 3D reconstruction from multi-view images. It initializes anisotropic Gaussian representations of complex scenes using sparse point clouds and optimizes these Gaussians using multiple perspective images. To apply 3DGS to panoramic images, we propose a panoramic view synthesis method. Specifically, each panoramic image is divided into multiple perspective images to obtain sparse point clouds and camera parameters through SfM. However, this division introduces stitching artifacts. To tackle this issue, we utilize a pre-trained depth estimation model to estimate depth as geometric guidance for depth supervision, where the inverse depth of each frame's SfM points is used to resolve scale and offset issues. We verify the proposed method on a custom indoor panoramic dataset. Compared to the original 3DGS, our method achieves relatively robust geometric structures and effectively reduces some artifacts. In addition, our method has the same effect after 7K iterations as 3DGS does after 30K iterations, and uses fewer Gaussians to represent the scene.