2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains
  for High-Fidelity Indoor Scene Reconstruction
Authors: name: Wanting Zhang, name: Haodong Xiang, name: Zhichao Liao, name: Xiansong Lai, name: Xinghui Li, name: Long Zeng

The reconstruction of indoor scenes remains challenging due to the inherent
complexity of spatial structures and the prevalence of textureless regions.
Recent advancements in 3D Gaussian Splatting have improved novel view synthesis
with accelerated processing but have yet to deliver comparable performance in
surface reconstruction. In this paper, we introduce 2DGS-Room, a novel method
leveraging 2D Gaussian Splatting for high-fidelity indoor scene reconstruction.
Specifically, we employ a seed-guided mechanism to control the distribution of
2D Gaussians, with the density of seed points dynamically optimized through
adaptive growth and pruning mechanisms. To further improve geometric accuracy,
we incorporate monocular depth and normal priors to provide constraints for
details and textureless regions respectively. Additionally, multi-view
consistency constraints are employed to mitigate artifacts and further enhance
reconstruction quality. Extensive experiments on ScanNet and ScanNet++ datasets
demonstrate that our method achieves state-of-the-art performance in indoor
scene reconstruction.