2DGS-Room：带几何约束的种子引导2D高斯飞溅，用于高保真室内场景重建

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

December 4, 2024

作者: Wanting Zhang, Haodong Xiang, Zhichao Liao, Xiansong Lai, Xinghui Li, Long Zeng

cs.AI

摘要

由于空间结构的固有复杂性和无纹理区域的普遍存在，室内场景的重建仍然具有挑战性。最近在3D高斯飘零技术方面取得的进展改善了新视角合成的加速处理，但在表面重建方面尚未提供可比较的性能。本文介绍了一种名为2DGS-Room的新方法，利用2D高斯飘零技术实现高保真度的室内场景重建。具体而言，我们采用种子引导机制来控制2D高斯分布，通过自适应生长和修剪机制动态优化种子点的密度。为了进一步提高几何精度，我们结合单目深度和法线先验来分别为细节和无纹理区域提供约束。此外，采用多视角一致性约束来减轻伪影并进一步增强重建质量。在ScanNet和ScanNet++数据集上进行的大量实验证明，我们的方法在室内场景重建方面实现了最先进的性能。

English

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.