Surface Reconstruction
84 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Surface Reconstruction
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Surface Reconstruction models and implementations
ANIM
Most implemented papers
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
Totoro97/NeuS
•
•
NeurIPS 2021
In NeuS, we propose to represent a surface as the zero-level set of a signed distance function (SDF) and develop a new volume rendering method to train a neural SDF representation.
Patch-based Progressive 3D Point Set Upsampling
yifita/3PU_pytorch
•
•
CVPR 2019
We present a detail-driven deep neural network for point set upsampling.
Controlling Neural Level Sets
In turn, the sample network can be used to incorporate the level set samples into a loss function of interest.
HumanMeshNet: Polygonal Mesh Recovery of Humans
3D Human Body Reconstruction from a monocular image is an important problem in computer vision with applications in virtual and augmented reality platforms, animation industry, en-commerce domain, etc.
Learned Point Cloud Geometry Compression
NJUVISION/PCGCv1
•
•
This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a. k. a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE).
PolyGen: An Autoregressive Generative Model of 3D Meshes
deepmind/deepmind-research
•
•
ICML 2020
Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development.
Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT
iMED-Lab/WRB-Net
•
•
We consider it to be the first work to detect angle-closure glaucoma by means of 3D representation.
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
autonomousvision/unisurf
•
•
ICCV 2021
At the same time, neural radiance fields have revolutionized novel view synthesis.
Score-Based Point Cloud Denoising (Learning Implicit Gradient Fields for Point Cloud Denoising)
luost26/score-denoise
•
•
ICCV 2021
Since $p * n$ is unknown at test-time, and we only need the score (i. e., the gradient of the log-probability function) to perform gradient ascent, we propose a neural network architecture to estimate the score of $p * n$ given only noisy point clouds as input.
Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds
Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points.
