Recently, 3D Gaussian Splatting (3DGS) has attracted widespread attention due
to its high-quality rendering, and ultra-fast training and rendering speed.
However, due to the unstructured and irregular nature of Gaussian point clouds,
it is difficult to guarantee geometric reconstruction accuracy and multi-view
consistency simply by relying on image reconstruction loss. Although many
studies on surface reconstruction based on 3DGS have emerged recently, the
quality of their meshes is generally unsatisfactory. To address this problem,
we propose a fast planar-based Gaussian splatting reconstruction representation
(PGSR) to achieve high-fidelity surface reconstruction while ensuring
high-quality rendering. Specifically, we first introduce an unbiased depth
rendering method, which directly renders the distance from the camera origin to
the Gaussian plane and the corresponding normal map based on the Gaussian
distribution of the point cloud, and divides the two to obtain the unbiased
depth. We then introduce single-view geometric, multi-view photometric, and
geometric regularization to preserve global geometric accuracy. We also propose
a camera exposure compensation model to cope with scenes with large
illumination variations. Experiments on indoor and outdoor scenes show that our
method achieves fast training and rendering while maintaining high-fidelity
rendering and geometric reconstruction, outperforming 3DGS-based and NeRF-based
methods.

通过引入基于平面的高斯点云猜测（PGSR）方法，本研究旨在实现高保真度的表面重建和高质量的渲染，以解决基于 3D 高斯斑点（3DGS）的重构方法中普遍存在的质量问题。实验结果表明，与基于 3DGS 和 NeRF 的方法相比，我们的方法在快速训练和渲染的同时保持了高保真度的渲染和几何重建效果。