Optical Coherence Tomography Angiography (OCTA) has been widely used by ophthalmologists for decision-making due to its superiority in providing caplillary details. Many of the OCTA imaging devices used in clinic provide high-quality 2D en face representations, while their 3D data quality are largely limited by low signal-to-noise ratio and strong projection artifacts, which restrict the performance of depth-resolved 3D analysis. In this paper, we propose a novel 2D-to-3D vessel reconstruction framework based on the 2D en face OCTA images. This framework takes advantage of the detailed 2D OCTA depth map for prediction and thus does not rely on any 3D volumetric data. Based on the data with available vessel depth labels, we first introduce a network with structure constraint blocks to estimate the depth map of blood vessels in other cross-domain en face OCTA data with unavailable labels. Afterwards, a depth adversarial adaptation module is proposed for better unsupervised cross-domain training, since images captured using different devices may suffer from varying image contrast and noise levels. Finally, vessels are reconstructed in 3D space by utilizing the estimated depth map and 2D vascular information. Experimental results demonstrate the effectiveness of our method and its potential to guide subsequent vascular analysis in 3D domain.