point cloud upsampling

31 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

PU-Net: Point Cloud Upsampling Network

yulequan/PU-Net CVPR 2018

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

liruihui/PU-GAN ICCV 2019

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

Point Cloud Upsampling via Disentangled Refinement

liruihui/Dis-PU CVPR 2021

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

Score-Based 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.

PU-Transformer: Point Cloud Upsampling Transformer

shiqiu0419/pu-transformer 24 Nov 2021

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

junshengzhou/levelsetudf ICCV 2023

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

guochengqian/PU-GCN CVPR 2021

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

ninaqy/PUGeo ECCV 2020

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

liuxinhai/spu-net 8 Dec 2020

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

GabrielleTse/PU-EVA ICCV 2021

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.