3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis
results while advancing real-time rendering performance. However, it relies
heavily on the quality of the initial point cloud, resulting in blurring and
needle-like artifacts in areas with insufficient initializing points. This is
mainly attributed to the point cloud growth condition in 3DGS that only
considers the average gradient magnitude of points from observable views,
thereby failing to grow for large Gaussians that are observable for many
viewpoints while many of them are only covered in the boundaries. To this end,
we propose a novel method, named Pixel-GS, to take into account the number of
pixels covered by the Gaussian in each view during the computation of the
growth condition. We regard the covered pixel numbers as the weights to
dynamically average the gradients from different views, such that the growth of
large Gaussians can be prompted. As a result, points within the areas with
insufficient initializing points can be grown more effectively, leading to a
more accurate and detailed reconstruction. In addition, we propose a simple yet
effective strategy to scale the gradient field according to the distance to the
camera, to suppress the growth of floaters near the camera. Extensive
experiments both qualitatively and quantitatively demonstrate that our method
achieves state-of-the-art rendering quality while maintaining real-time
rendering speed, on the challenging Mip-NeRF 360 and Tanks & Temples datasets.

提出了一种名为 Pixel-GS 的新方法，通过考虑每个视图中高斯函数覆盖的像素数量来计算增长条件，从而促进大型高斯函数的增长，实现了更精确、更详细的重构，同时保持实时渲染速度和最新的渲染质量。