Existing neural implicit surface reconstruction methods have achieved impressive performance in multi-view 3D reconstruction by leveraging explicit geometry priors such as depth maps or point clouds as regularization. However, the reconstruction results still lack fine details because of the over-smoothed depth map or sparse point cloud. In this work, we propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces. The advantage of 3D Gaussian Splatting is that it can generate dense point clouds with detailed structure. Nonetheless, a naive adoption of 3D Gaussian Splatting can fail since the generated points are the centers of 3D Gaussians that do not necessarily lie on the surface. We thus introduce a scale regularizer to pull the centers close to the surface by enforcing the 3D Gaussians to be extremely thin. Moreover, we propose to refine the point cloud from 3D Gaussians Splatting with the normal priors from the surface predicted by neural implicit models instead of using a fixed set of points as guidance. Consequently, the quality of surface reconstruction improves from the guidance of the more accurate 3D Gaussian splatting. By jointly optimizing the 3D Gaussian Splatting and the neural implicit model, our approach benefits from both representations and generates complete surfaces with intricate details. Experiments on Tanks and Temples verify the effectiveness of our proposed method.
现有的神经隐式表面重建方法在多视图3D重建中取得了令人印象深刻的表现，这归功于利用显式几何先验（如深度图或点云）作为正则化。然而，重建结果仍然缺乏细节，因为深度图过于平滑或点云稀疏。在这项工作中，我们提出了一种以3D高斯喷溅为指导的神经隐式表面重建流程，以恢复高度详细的表面。3D高斯喷溅的优势在于它能生成具有详细结构的密集点云。尽管如此，简单地采用3D高斯喷溅可能会失败，因为生成的点是3D高斯的中心，这些中心不一定位于表面上。因此，我们引入了一个尺度正则化器，通过强制3D高斯变得极其细长，将中心点拉近到表面。此外，我们提出使用由神经隐式模型预测的表面法线先验来细化3D高斯喷溅产生的点云，而不是使用固定点集作为指导。结果是，更准确的3D高斯喷溅指导下，表面重建质量得到了提升。通过联合优化3D高斯喷溅和神经隐式模型，我们的方法受益于两种表示，并生成了具有复杂细节的完整表面。在“坦克与庙宇”数据集上的实验验证了我们提出方法的有效性。