A Unified Framework for Surface Reconstruction
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Updated
Oct 24, 2023 - Python
A Unified Framework for Surface Reconstruction
[CVPR 2024] Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
[SIGGRAPH'24] 2D Gaussian Splatting for Geometrically Accurate Radiance Fields
A Modular Framework for 3D Gaussian Splatting and Beyond
[ECCV'20] Convolutional Occupancy Networks
Polygonal Surface Reconstruction from Point Clouds
[NeurIPS'21] Shape As Points: A Differentiable Poisson Solver
[NeurIPS'22] MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
ManifoldPlus: A Robust and Scalable Watertight Manifold Surface Generation Method for Triangle Soups
Gaussian Opacity Fields: Efficient and Compact Surface Reconstruction in Unbounded Scenes
Intrinsic3D - High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting (ICCV 2017)
Learning Implicit Surfaces from Point Clouds (ECCV 2020)
A command line tool to transform a DICOM volume into a 3d surface mesh (obj, stl or ply). Several mesh processing routines can be enabled, such as mesh reduction, smoothing or cleaning. Works on Linux, OSX and Windows.
Poisson Surface Reconstruction for LiDAR Odometry and Mapping
Massively Parallel Multiview Stereopsis by Surface Normal Diffusion
Volume rendering based surface reconstruction using Unsigned Distance Fields
scikit-fmm is a Python extension module which implements the fast marching method.
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces (CVPR 2023)
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details (NeurIPS 2022)
Large-scale LoD2 Building Reconstruction from Airborne LiDAR Point Clouds
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