Recently, there has been a growing interest in learningbased explicit methods due to their ability to respect the original input and preserve details. However, the connectivity on complex structures is still difficult to infer due to the limited local shape perception, resulting in artifacts and non-watertight triangles. In this paper, we present a novel learning-based method with Delaunay triangulation to achieve high-precision reconstruction. We model the Delaunay triangulation as a dual graph, extract local geometric information from the points, and embed it into the structural representation of Delaunay triangulation in an organic way, benefiting fine-grained details reconstruction. To encourage neighborhood information interaction of edges and nodes in the graph, we introduce a local graph iteration algorithm, which is a variant of graph neural network. Moreover, a geometric constraint loss further improves the classification of tetrahedrons. Benefiting from our fully local network, a scaling strategy is designed to enable large-scale reconstruction. Experiments show that our method yields watertight and high-quality meshes. Especially for some thin structures and sharp edges, our method shows better performance than the current state-of-the-art methods. Furthermore, it has a strong adaptability to point clouds of different densities.

Learning-based surface reconstruction methods have received considerable attention in recent years due to their excellent expressiveness. However, existing learning-based methods lack scalability in processing large-scale point clouds. This paper proposes a novel scalable learning-based 3D surface reconstruction method based on octree, called SSRNet. SSRNet works in a scalable reconstruction pipeline, which divides oriented point clouds into different local parts and then processes them in parallel. Accommodating this scalable design pattern, SSRNet constructs local geometric features for octree vertices. Such features comprise the relation between the vertices and the implicit surface, ensuring geometric perception. Focusing on local geometric information also enables the network to avoid the overfitting problem and generalize well on different datasets. Finally, as a learning-based method, SSRNet can process large-scale point clouds in a short time. And to further solve the efficiency problem, we provide a lightweight and efficient version that is about five times faster while maintaining reconstruction performance. Experiments show that our methods achieve state-of-the-art performance with outstanding efficiency.