PyGS：基于金字塔式三维高斯溅射的大规模场景表示
PyGS: Large-scale Scene Representation with Pyramidal 3D Gaussian Splatting
AI总结：
针对大规模场景三维高斯溅射的尺度与初始化难题，提出金字塔式高斯层次结构与NeRF初始化，实现高质量渲染且速度提升超400倍。
摘要：
神经辐射场（NeRFs）在合成大规模场景的逼真图像方面表现出卓越的能力。然而，它们常常受到细节丢失和渲染时间长的困扰。最近，三维高斯溅射（3D Gaussian Splatting）作为一种强有力的替代方案被引入，实现了高保真视觉效果和加速渲染性能。尽管如此，扩展三维高斯溅射仍面临诸多挑战。具体而言，大规模场景需要处理跨多个尺度和不同视角的物体集成，这往往导致高斯在平衡细节层次时效能受损。此外，通过COLMAP从大规模数据集生成初始化点在计算上要求高，且容易产生不完整的重建。为解决这些挑战，我们提出了带NeRF初始化的金字塔式三维高斯溅射（PyGS）。我们的方法通过金字塔方式排列的高斯层次结构来表示场景。金字塔顶层由少数大型高斯组成，而每一后续层则包含更密集的小型高斯集合。我们通过对快速训练的基于网格的NeRF进行不同频率的采样，有效地初始化这些金字塔式高斯。我们将这些金字塔式高斯分组为簇，并使用一个紧凑的加权网络在渲染过程中动态确定每个簇的每个金字塔层级在考虑相机视角下的影响。我们的方法在多个大规模数据集上实现了显著的性能飞跃，并且渲染时间比当前最先进的方法快400倍以上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
