PG-SAG: Parallel Gaussian Splatting for Fine-Grained Large-Scale Urban Buildings Reconstruction via Semantic-Aware Grouping
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.
3D 高斯喷射 (3DGS) 已成为实时新颖视图合成领域的一种变革性方法。基于 3DGS，近期的研究通过基于空间的分区策略应对大规模场景，以降低视频内存和优化时间成本。在本文中，我们提出了一种并行高斯喷射方法，称为 PG-SAG，它充分利用语义信息进行分区和高斯核优化，实现了对大规模城市区域的细粒度建筑表面重建，而无需对原始图像分辨率进行下采样。 首先，我们利用跨模态模型 Language Segment Anything 提取建筑区域的分割掩码。然后，根据已注册图像中的可见性检查，将分割的建筑区域划分为子区域。这些子区域的高斯核通过掩码像素进行并行优化。此外，我们重新设计了法向损失函数，专注于掩码检测边缘，缓解边缘处法向量模糊的问题。 最后，为了改进 3D 高斯的优化，我们引入了一种 梯度约束的负载平衡损失，该损失考虑了相应场景的复杂性，有效减少了像素并行渲染阶段的线程等待时间，同时降低了重建损失。 我们在多个城市数据集上进行了广泛实验，结果表明，与几种最先进的基于 3DGS 的方法相比，PG-SAG 在建筑表面重建性能上表现更为优越。