point cloud upsampling
28 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in point cloud upsampling
PU1K
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
luost26/score-denoise
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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
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
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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
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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
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
GabrielleTse/PU-EVA
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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.
