IFA-Net: Isomerous Feature-aware Network for Single-view 3D Reconstruction

Single-view 3D reconstruction has long been an intractable and fundamental problem in computer vision. Objects with complex topological structures are difficult to be accurately reconstructed, which makes the existing methods suffer from blurred shape boundaries between multiple components in the object. Recently, convolutional neural network and vision transformer have begun to appear in the field of 3D reconstruction and have been widely used with excellent performance. However, the existing transformer-based methods mainly focus on the global long-term context dependency, and ignore the local details of the part space features, resulting in poor reconstruction of the detail part. In this paper, we propose a novel dual-branch network architecture, called IFA-Net, to capture local spatial perception information and retain global structural features for singleview 3D reconstruction. In addition, we propose an isomerous feature-aware module, which enables the dynamic fusion of different resolution features under the two branches. Thus, high-fidelity and detail-rich 3D object reconstruction can be achieved. Extensive experimental results demonstrate that our method is able to produce high-quality voxels, particularly with diverse topologies, as compared with the state-of-the-art methods.