A Unified Framework for Surface Reconstruction
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Updated
Jul 11, 2024 - Python
A Unified Framework for Surface Reconstruction
[ECCV'20] Convolutional Occupancy Networks
[SIGGRAPH'24] 2D Gaussian Splatting for Geometrically Accurate Radiance Fields
scikit-fmm is a Python extension module which implements the fast marching method.
Poisson Surface Reconstruction for LiDAR Odometry and Mapping
[NeurIPS'22] MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
Learning Implicit Surfaces from Point Clouds (ECCV 2020)
[NeurIPS'21] Shape As Points: A Differentiable Poisson Solver
Structural MRI PREProcessing (sMRIPrep) workflows for NIPreps (NeuroImaging PREProcessing tools)
Gaussian Opacity Fields: Efficient and Compact Surface Reconstruction in Unbounded Scenes
code for "PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction"
Volume rendering based surface reconstruction using Unsigned Distance Fields
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details (NeurIPS 2022)
Official code for FOUND: Foot Optimisation with Uncertain Normals for Surface Deformation using Synthetic Data
[CVPR2024] NARUTO: Neural Active Reconstruction
[SGP 2021] Scalable Surface Reconstruction with Delaunay-Graph Neural Networks
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces (CVPR 2023)
ICCV2021 Oral SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks
Import a point cloud file and perform poisson 3D surface reconstruction algorithm, integrated with third-party libraries (e.g. open3d, pymeshlab...)
3D medical imaging reconstruction software
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