2DGS-Room：用于高保真室内场景重建的、带有几何约束的种子引导二维高斯泼溅
2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains for High-Fidelity Indoor Scene Reconstruction
AI总结：
针对室内复杂结构与无纹理区域重建难题，提出2DGS-Room，通过种子引导的二维高斯分布控制、深度法向先验及多视角一致性约束，在ScanNet与ScanNet++上达到SOTA。
摘要：
由于空间结构固有的复杂性以及无纹理区域的普遍存在，室内场景重建仍然具有挑战性。3D Gaussian Splatting（三维高斯泼溅）的最新进展通过加速处理改善了新视角合成，但在表面重建方面尚未提供可与之相比的性能。本文提出2DGS-Room，这是一种利用2D Gaussian Splatting（二维高斯泼溅）进行高保真室内场景重建的新颖方法。具体而言，我们采用种子引导机制来控制二维高斯的分布，并通过自适应增长和剪枝机制动态优化种子点的密度。为进一步提升几何精度，我们引入单目深度先验和法先验，分别为细节区域和无纹理区域提供约束。此外，还采用多视角一致性约束来减轻伪影并进一步提高重建质量。在ScanNet和ScanNet++数据集上的大量实验表明，我们的方法在室内场景重建中取得了最先进的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
