# point cloud upsampling

31 papers with code • 0 benchmarks • 1 datasets

## Benchmarks

These leaderboards are used to track progress in point cloud upsampling
## Most implemented papers

# PU-Net: Point Cloud Upsampling Network

Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.

# PU-GAN: a Point Cloud Upsampling Adversarial Network

Point clouds acquired from range scans are often sparse, noisy, and non-uniform.

# Point Cloud Upsampling via Disentangled Refinement

Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy.

# Score-Based 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.

# PU-Transformer: Point Cloud Upsampling Transformer

Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines.

# Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection

We pull the non-zero level sets onto the zero level set with gradient constraints which align gradients over different level sets and correct unsigned distance errors on the zero level set, leading to a smoother and more continuous unsigned distance field.

# PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks

We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN.

# PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling

Matrix $\mathbf T$ approximates the augmented Jacobian matrix of a local parameterization and builds a one-to-one correspondence between the 2D parametric domain and the 3D tangent plane so that we can lift the adaptively distributed 2D samples (which are also learned from data) to 3D space.

# SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization

The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets.

# PU-EVA: An Edge-Vector Based Approximation Solution for Flexible-Scale Point Cloud Upsampling

In this work, the arbitrary point clouds upsampling rates are achieved via edge-vector based affine combinations, and a novel design of Edge-Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed.