We propose CrossSDF, a novel approach for reconstructing a 3D signed-distance field from 2D cross-sections. The input is a set of 2D cross-sections that sample an unobserved ground-truth geometric object by planar intersection (denoted as black lines overlayed on the ground truth Alveolis structure on the left). CrossSDF (middle) accurately reconstructs thin structures without breakages, oversmoothing, or cross-sectional artifacts observed in competing methods (right).

We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections.This allows us to introduce useful priors to generate valid meshes. However those priors prevent the generative model from learning details limiting the applicability of the method to real-world scenarios. To alleviate that problem we make the transformation learnable and constrain the rendered depth map to be consistent with the zero-level set of the SDF. Through the lens of adversarial training we encourage the network to produce higher fidelity details on the output meshes.