2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains for High-Fidelity Indoor Scene Reconstruction
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.
室内场景的重建因空间结构的复杂性和无纹理区域的普遍存在而充满挑战。近年来，三维高斯喷溅（3D Gaussian Splatting, 3DGS）在加速处理的同时改进了新视角合成，但在表面重建性能上仍未达到同等水平。 本文提出了2DGS-Room，一种利用二维高斯喷溅（2D Gaussian Splatting）实现高保真室内场景重建的新方法。具体而言，我们采用种子引导机制控制二维高斯的分布，通过自适应增长和修剪机制动态优化种子点的密度。为进一步提升几何精度，我们引入单目深度和法线先验，分别为细节和无纹理区域提供约束。此外，利用多视图一致性约束减少伪影并进一步增强重建质量。 在ScanNet和ScanNet++数据集上的大量实验表明，2DGS-Room在室内场景重建中达到了当前最先进的性能，显著提高了几何和纹理细节的保真度，为室内场景的高精度重建提供了一种有效解决方案。