Neural Radiance Fields (NeRFs) have demonstrated remarkable proficiency in
synthesizing photorealistic images of large-scale scenes. However, they are
often plagued by a loss of fine details and long rendering durations. 3D
Gaussian Splatting has recently been introduced as a potent alternative,
achieving both high-fidelity visual results and accelerated rendering
performance. Nonetheless, scaling 3D Gaussian Splatting is fraught with
challenges. Specifically, large-scale scenes grapples with the integration of
objects across multiple scales and disparate viewpoints, which often leads to
compromised efficacy as the Gaussians need to balance between detail levels.
Furthermore, the generation of initialization points via COLMAP from
large-scale dataset is both computationally demanding and prone to incomplete
reconstructions. To address these challenges, we present Pyramidal 3D Gaussian
Splatting (PyGS) with NeRF Initialization. Our approach represent the scene
with a hierarchical assembly of Gaussians arranged in a pyramidal fashion. The
top level of the pyramid is composed of a few large Gaussians, while each
subsequent layer accommodates a denser collection of smaller Gaussians. We
effectively initialize these pyramidal Gaussians through sampling a rapidly
trained grid-based NeRF at various frequencies. We group these pyramidal
Gaussians into clusters and use a compact weighting network to dynamically
determine the influence of each pyramid level of each cluster considering
camera viewpoint during rendering. Our method achieves a significant
performance leap across multiple large-scale datasets and attains a rendering
time that is over 400 times faster than current state-of-the-art approaches.

通过使用金字塔式组装高斯函数的 3D 高斯喷射 (PyGS)，结合 NeRF 初始化，我们在多个大规模数据集上实现了显著的性能提升，并获得超过当前最先进方法 400 倍以上的渲染时间。