Existing three-dimensional surface reconstruction methods that rely on gradient fields often exhibit relatively low accuracy in practical applications, primarily due to the absence of physical constraints and their susceptibility to noise interference. To address these challenges, this paper introduces a dual-branch model based on ResUNet, referred to as DS-ResUNet. This model processes gradient information in both the x and y directions through separate dual-branch encoders and incorporates the gradient equation as a physical constraint during the reconstruction process. This approach ensures that the surface reconstruction results adhere to physical laws while effectively capturing surface features.Numerical simulations with added noise demonstrate that, compared to the DUNet method, DS-ResUNet offers significant advantages in predicting surface shapes: the mean squared error is reduced by approximately 60.94% on average; the relative root mean square error decreases by an average of 8.15%; the relative peak-to-valley value is diminished by an average of 52.37%; and there is an increase of 2.19% in the structural similarity index. The proposed DS-ResUNet model has successfully enhanced the accuracy of three-dimensional surface reconstruction across both simulated environments and real-world scenarios.