Projects sharing features with TowardsBetterGradient

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 repo for the implementation of Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction, Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, Wenbing Tao (* Equal Contribution), NeurIPS 2022. We will release our pretrained models soon!

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

Recovering Fine Details for Neural Implicit Surface Reconstruction (WACV2023)

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