Surface Reconstruction

84 papers with code • 1 benchmarks • 1 datasets

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Use these libraries to find Surface Reconstruction models and implementations
2 papers


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

matanatz/SAL NeurIPS 2019

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

yudhik11/HumanMeshNet 19 Aug 2019

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 26 Sep 2019

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 9 Jun 2020

We consider it to be the first work to detect angle-closure glaucoma by means of 3D representation.

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

fbernardpi/sparsePdmFitting 26 Feb 2016

Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points.