Gaussian splatting methods have become increasingly popular for neural reconstruction of the real world. However, they are often limited in scale and resolution due to compute and memory constraints. We present a multi-GPU Gaussian splatting approach that scales reconstruction to higher resolutions and larger scenes while abstracting away the code complexity typically associated with distributing a model. To accomplish this, we propose a PyTorch backend that distributes the Gaussian parameters and splatting operators across GPUs via CUDA unified memory and NVLink. Because distribution occurs at the operator level, the model code requires no explicit cross-device communication. More broadly, the backend exposes multiple GPUs as an aggregate PyTorch device and supports other PyTorch operators. We demonstrate city-scale reconstructions with street-level detail consisting of over 1 billion Gaussian splats, more than 25 times as many as the current state of the art.

翻译：高斯喷溅方法在真实世界的神经重建中日益普及。然而，由于计算和内存限制，其规模与分辨率往往受限。我们提出一种多GPU高斯喷溅方法，可将重建拓展至更高分辨率和更大场景，同时规避分布式模型通常涉及的代码复杂性。为实现这一目标，我们提出一种PyTorch后端，通过CUDA统一内存和NVLink将高斯参数与喷溅算子分布到各GPU。由于分布发生在算子层面，模型代码无需显式的跨设备通信。更广泛而言，该后端将多个GPU暴露为聚合的PyTorch设备，并支持其他PyTorch算子。我们展示了包含超过10亿个高斯喷溅体、比当前最先进方法规模高25倍以上的城市级重建，并具备街道级细节。