# Surface Reconstruction

84 papers with code • 1 benchmarks • 1 datasets

## Libraries

Use these libraries to find Surface Reconstruction models and implementations## Most implemented papers

# NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

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

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

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

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

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

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)

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.