用种子引导高斯点分布，实现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
- 通过种子引导控制2D高斯点分布，动态增删优化密度
- 结合单目深度和法线先验，提升无纹理区域精度
- 适合需要精细几何重建的室内场景应用
由于空间结构复杂且普遍存在无纹理区域，室内场景重建仍具挑战。尽管3D高斯溅射在新视角合成方面加速了处理速度，但在表面重建上尚未达到同等性能。本文提出2DGS-Room，一种基于2D高斯溅射的高保真室内场景重建方法。具体而言，采用种子引导机制控制2D高斯点分布，通过自适应增长与修剪机制动态优化种子点密度。为进一步提升几何精度，引入单目深度先验约束细节，法线先验约束无纹理区域。同时，利用多视图一致性约束减少伪影，进一步提升重建质量。在ScanNet和ScanNet++数据集上的大量实验表明，本方法在室内场景重建中达到当前最优性能。
原文摘要 · Abstract (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.
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