Efficient 3D Surface Super-resolution via Normal-based Multimodal Restoration

High-fidelity 3D surface is essential for vision tasks across various domains such as medical imaging, cultural heritage preservation, quality inspection, virtual reality, and autonomous navigation. However, the intricate nature of 3D data representations poses significant challenges in restoring diverse 3D surfaces while capturing fine-grained geometric details at a low cost. This paper introduces an efficient multimodal normal-based 3D surface super-resolution (mn3DSSR) framework, designed to address the challenges of microgeometry enhancement and computational overhead. Specifically, we have constructed one of the largest normalbased multimodal dataset, ensuring superior data quality and diversity through meticulous subjective selection. Furthermore, we explore a new two-branch multimodal alignment approach along with a multimodal split fusion module to mitigate computational complexity while improving restoration performances. To address the limitations associated with normal-based multimodal learning, we develop novel normal-induced loss functions that facilitate geometric consistency and improve feature alignment. Extensive experiments conducted on seven benchmark datasets across four different 3D data representations demonstrate that mn3DSSR consistently outperforms state-ofthe-art super-resolution methods in terms of restoration accuracy with high computational efficiency.