Robust Gaussian Surface Reconstruction with Semantic Aware Progressive Propagation

We propose GSAPro, a Gaussian Splatting based 3D surface reconstruction framework that exhibits robustness across diverse scales of scenes. Previous research has leveraged photometric consistency constraints or prior information as guidance to enhance the reconstruction accuracy. However, error estimation and noise inevitably exist in these priors. Applying a strict geometric filter removes a large amount of reliable information, resulting in a deterioration of the quality of guided reconstruction. Regarding possible errors in the initial guidance, GSAPro can continuously improve the accuracy of the guidance through a joint optimization strategy. The Gaussian Branch integrates reliable geometric and color constraints, thus providing more accurate geometric parameters for the Prior Branch compared to its current state guidance parameters. The Prior Branch, through photometric selection and propagation, obtains more accurate geometric parameters from the state geometric parameters and rendered parameters. Then GSAPro uses these parameters to guide the optimization of the Gaussian Branch. Regarding the problem of noise existing in the guidance, we train the Semantic Aware Module to predict the noise by utilizing the image information, thus improving the accuracy. Moreover, we also introduce a Distillation Module to mitigate the excessive splitting of Gaussians that is caused by the implementation of additional constraints. Experiments demonstrate that our method exhibits SOTA performance and has stronger robustness against scenes of different scales.