Open-source alternatives to Geo Neus

Neuralangelo is a neural surface reconstruction framework that transforms two-dimensional image sequences and multi-view photography into high-fidelity 3D meshes. It implements a pipeline for training neural radiance fields to represent complex scenes as digital geometry. The project utilizes a signed distance function for surface representation and multi-resolution hash encoding to capture both coarse and fine geometric details. It employs differentiable volume rendering and gradient-based eikonal regularization to ensure the learned distance functions remain physically plausible. The syste

This is the official implementation for the ICCV 2021 paper Learning Signed Distance Field for Multi-view Surface Reconstruction

This repository contains the code to reproduce the results from the paper. Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment.

Recovering Fine Details for Neural Implicit Surface Reconstruction (WACV2023)

Multi-view 3D reconstruction using neural rendering. Unofficial implementation of UNISURF, VolSDF, NeuS and more.

Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction

HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details