G2IFu: Graph-based implicit function for single-view 3D reconstruction

As the demand for 3D models increases, there is growing interest in reconstructing 3D objects from images using AI. In this paper, we propose G2IFu, a graph-based implicit function that successfully reconstructs a highly detailed 3D object mesh from a single image. Unlike previous methods that learn implicit functions with points, G2IFu aims to map graphs to implicit values. We make the following contributions: (1) Compared to independent 3D points, graphs have a larger perception space and contain specific spatial structure information. Therefore, we extend a 3D point p to a graph Gp by generating hypothesis points and establishing edges between them. We then predict the corresponding implicit value using a graph convolution network. Our experiments show that this method can effectively improve the prediction accuracy of implicit functions. (2) We introduce a prior boundary loss based on Gp to make the network pay more attention to the "key"points near the shape surface. To the best of our knowledge, G2IFu is the first model that introduces a graph into neural implicit representation. (3) Inspired by previous methods, we utilize the image's global and local features to initialize Gp. We also introduce a self-attention module into G2IFu for better performance. We conduct experiments on the ShapeNet dataset and demonstrate that G2IFu can generate higher-quality 3D object shapes than previous single-view reconstruction methods. Additionally, we extend G2IFu to multi-view 3D reconstruction and achieve good performance.