MNSRNet: Multimodal Transformer Network for 3D Surface Super-Resolution

With the rapid development of display technology, it has become an urgent need to obtain realistic 3D surfaces with as high-quality as possible. Due to the unstructured and irregular nature of 3D object data, it is usually difficult to obtain high-quality surface details and geometry textures at a low cost. In this article, we propose an effective multimodal-driven deep neural network to perform 3D surface super-resolution in 2D normal domain, which is simple, accurate, and robust to the above difficulty. To leverage the multimodal information from different perspectives, we jointly consider the texture, depth, and normal modalities to simultaneously restore fine-grained surface details as well as preserve geometry structures. To better utilize the cross-modality information, we explore a two-bridge normal method with a transformer structure for feature alignment, and investigate an affine transform module for fusing multimodal features. Extensive experimental results on public and our newly constructed photometric stereo dataset demonstrate that the proposed method delivers promising surface geometry details compared with nine competitive schemes.