Papers
arxiv:2412.03428

2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains for High-Fidelity Indoor Scene Reconstruction

Published on Dec 4
· Submitted by Valentina-Zhang on Dec 9
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Abstract

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.

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😊Given multi-view posed images, we improve 2DGS to achieve high-fidelity geometric reconstruction for indoor scenes.

🌟Highlights:

  • We propose 2DGS-Room, a novel method for indoor scene reconstruction based on 2DGS, which leverages the seed points maintaining the scene structure to guide the distribution and density of 2D Gaussians.
  • We introduce monocular depth and normal priors to provide geometric cues, improving the reconstruction of detailed areas and textureless regions respectively.
  • We employ multi-view constraints incorporating geometric and photometric consistency to further enhance the reconstruction quality.
  • Our method achieves high-quality surface reconstruction for indoor scenes. Extensive experiments on indoor scene datasets show that our method achieves state-of-the-art in multiple evaluation metrics.

🏠Project page: https://valentina-zhang.github.io/2DGS-Room/
📃Paper: https://arxiv.org/pdf/2412.03428
💻Code: https://github.com/Valentina-Zhang/2DGS-Room
[Our code is coming soon🚀]

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