Learning neural implicit surfaces with local probability standard variance

Abstract Reconstructing geometric shapes from sparse multiview has always been a challenging task. With the development of neural implicit surfaces, geometry‐based volume rendering surface reconstruction methods have been proven to be able to reconstruct high‐quality surfaces. However, existing geometry‐based reconstruction methods completely associate volume density with signed distance function or unsigned distance function, resulting in the same volume density peak that can only be reconstructed near the object surface. When there are transparent surfaces in the scene, existing methods prioritize the reconstruction of opaque surfaces, neglecting the reconstruction of transparent surfaces, which is disadvantageous when reconstructing real scenes. To solve this problem, we introduce local probability standard variance, which calculates volume density together with signed distance function. In this way, it can reconstruct the volume density that matches the transparency characteristics of the object surface. The method can reconstruct the surface of transparent objects, and experiments on two transparent surface datasets show that the method performs better.