3D Gaussian Splatting (3DGS) has emerged as a transformative method in the field of real-time novel synthesis. Based on 3DGS, recent advancements cope with large-scale scenes via spatial-based partition strategy to reduce video memory and optimization time costs. In this work, we introduce a parallel Gaussian splatting method, termed PG-SAG, which fully exploits semantic cues for both partitioning and Gaussian kernel optimization, enabling fine-grained building surface reconstruction of large-scale urban areas without downsampling the original image resolution. First, the Cross-modal model - Language Segment Anything is leveraged to segment building masks. Then, the segmented building regions is grouped into sub-regions according to the visibility check across registered images. The Gaussian kernels for these sub-regions are optimized in parallel with masked pixels. In addition, the normal loss is re-formulated for the detected edges of masks to alleviate the ambiguities in normal vectors on edges. Finally, to improve the optimization of 3D Gaussians, we introduce a gradient-constrained balance-load loss that accounts for the complexity of the corresponding scenes, effectively minimizing the thread waiting time in the pixel-parallel rendering stage as well as the reconstruction lost. Extensive experiments are tested on various urban datasets, the results demonstrated the superior performance of our PG-SAG on building surface reconstruction, compared to several state-of-the-art 3DGS-based methods. Project Web:https://github.com/TFWang-9527/PG-SAG.

翻译：三维高斯溅射（3DGS）已成为实时新视角合成领域的一种变革性方法。基于3DGS，近期研究通过基于空间的分区策略处理大规模场景，以降低显存占用和优化时间成本。本文提出一种并行高斯溅射方法PG-SAG，该方法充分利用语义线索进行分区和高斯核优化，能够在保持原始图像分辨率的前提下实现大规模城区的细粒度建筑表面重建。首先，利用跨模态模型——语言分割万物（Language Segment Anything）分割建筑掩码。随后，根据已配准图像间的可见性检查，将分割出的建筑区域分组为多个子区域。这些子区域对应的高斯核通过掩码像素进行并行优化。此外，针对掩码边缘检测结果重新构建了法向损失函数，以缓解边缘区域法向矢量的歧义性。最后，为提升三维高斯优化效果，我们引入一种梯度约束的负载均衡损失函数，该函数考虑对应场景的复杂度，能有效减少像素并行渲染阶段的线程等待时间并降低重建误差。在多个城市数据集上的大量实验表明，相较于多种基于3DGS的先进方法，我们的PG-SAG在建筑表面重建方面展现出优越性能。项目网址：https://github.com/TFWang-9527/PG-SAG。