  Over the last few decades, image-based building surface reconstruction has
garnered substantial research interest and has been applied across various
fields, such as heritage preservation, architectural planning, etc. Compared to
the traditional photogrammetric and NeRF-based solutions, recently, Gaussian
fields-based methods have exhibited significant potential in generating surface
meshes due to their time-efficient training and detailed 3D information
preservation. However, most gaussian fields-based methods are trained with all
image pixels, encompassing building and nonbuilding areas, which results in a
significant noise for building meshes and degeneration in time efficiency. This
paper proposes a novel framework, Masked Gaussian Fields (MGFs), designed to
generate accurate surface reconstruction for building in a time-efficient way.
The framework first applies EfficientSAM and COLMAP to generate multi-level
masks of building and the corresponding masked point clouds. Subsequently, the
masked gaussian fields are trained by integrating two innovative losses: a
multi-level perceptual masked loss focused on constructing building regions and
a boundary loss aimed at enhancing the details of the boundaries between
different masks. Finally, we improve the tetrahedral surface mesh extraction
method based on the masked gaussian spheres. Comprehensive experiments on UAV
images demonstrate that, compared to the traditional method and several
NeRF-based and Gaussian-based SOTA solutions, our approach significantly
improves both the accuracy and efficiency of building surface reconstruction.
Notably, as a byproduct, there is an additional gain in the novel view
synthesis of building.
