A Unified Framework for Surface Reconstruction
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Updated
Jul 11, 2024 - Python
A Unified Framework for Surface Reconstruction
[NeurIPS 2023] Official code of "One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization"
A Modular Framework for 3D Gaussian Splatting and Beyond
[NeurIPS'22] MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details (NeurIPS 2022)
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces (CVPR 2023)
[CVPR 2023] Multi-View Azimuth Stereo via Tangent Space Consistency
(PG2023/CGF) This is the official PyTorch implementation of PG2023/CGF paper: GA-Sketching: Shape Modeling from Multi-View Sketching with Geometry-Aligned Deep Implicit Functions
Multi-View RGB-based Recognition and Reconstruction in PyTorch
(Arxiv 2023) Optimized View and Geometry Distillation from Multi-view Diffuser
Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects
High-Fidelity 3D Reconstruction via Graph Optimization. A Multi-Frame Registration and reconstruction algorithm.
A lightweight neural network for multi-view classification and reconstruction.
[AAAI'24] NeuSurf: On-Surface Priors for Neural Surface Reconstruction from Sparse Input Views
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