This repository implements a CVPR 2022 Oral paper on neural 3D reconstruction using signed distance functions (SDF) under the Manhattan-world assumption. The method reconstructs 3D scenes from multi-view RGB images by combining implicit neural representations with geometric priors about structural regularity in man-made environments. It provides training and evaluation code for the ScanNet dataset.