PG-SAG: Parallel Gaussian Splatting for Fine-Grained Large-Scale Urban Buildings Reconstruction via Semantic-Aware Grouping
Journal:
arXiv
Published Date:
Jan 3, 2025
Abstract
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.
