Spelling suggestions: "subject:"point cloud downsampling""
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Leveraging Graph Convolutional Networks for Point Cloud UpsamplingQian, Guocheng 16 November 2020 (has links)
Due to hardware limitations, 3D sensors like LiDAR often produce sparse and
noisy point clouds. Point cloud upsampling is the task of converting such point
clouds into dense and clean ones. This thesis tackles the problem of point cloud upsampling
using deep neural networks. The effectiveness of a point cloud upsampling
neural network heavily relies on the upsampling module and the feature extractor used
therein. In this thesis, I propose a novel point upsampling module, called NodeShuffle.
NodeShuffle leverages Graph Convolutional Networks (GCNs) to better encode
local point information from point neighborhoods. NodeShuffle is versatile and can
be incorporated into any point cloud upsampling pipeline. Extensive experiments
show how NodeShuffle consistently improves the performance of previous upsampling
methods. I also propose a new GCN-based multi-scale feature extractor, called Inception
DenseGCN. By aggregating features at multiple scales, Inception DenseGCN
learns a hierarchical feature representation and enables further performance gains. I
combine Inception DenseGCN with NodeShuffle into the proposed point cloud upsampling
network called PU-GCN. PU-GCN sets new state-of-art performance with
much fewer parameters and more efficient inference.
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Generative adversarial network for point cloud upsamplingWidell Delgado, Edison January 2024 (has links)
Point clouds are a widely used system for the collection and application of 3D data. But most timesthe data gathered is too scarce to reliably be used in any application. Therefore this thesis presentsa GAN based upsampling method within a patch based approach together with a GCN based featureextractor, in an attempt to enhance the density and reliability of point cloud data. Our approachis rigorously compared with existing methods to compare the performance. The thesis also makescorrelations between input sizes and how the quality of the inputs affects the upsampled result. TheGAN is also applied to real-world data to assess the viability of its current state, and to test how it isaffected by the interference that occurs in an unsupervised scenario.
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Applications of Graph Convolutional Networks and DeepGCNs in Point Cloud Part Segmentation and UpsamplingAbualshour, Abdulellah 18 April 2020 (has links)
Graph convolutional networks (GCNs) showed promising results in learning from point cloud data. Applications of GCNs include point cloud classification, point cloud segmentation, point cloud upsampling, and more. Recently, the introduction of Deep Graph Convolutional Networks (DeepGCNs) allowed GCNs to go deeper, and thus resulted in better graph learning while avoiding the vanishing gradient problem in GCNs. By adapting impactful methods from convolutional neural networks (CNNs) such as residual connections, dense connections, and dilated convolutions, DeepGCNs allowed GCNs to learn better from non-Euclidean data. In addition, deep learning methods proved very effective in the task of point cloud upsampling. Unlike traditional optimization-based methods, deep learning-based methods to point cloud upsampling does not rely on priors nor hand-crafted features to learn how to upsample point clouds. In this thesis, I discuss the impact and show the performance results of DeepGCNs in the task of point cloud part segmentation on PartNet dataset. I also illustrate the significance of using GCNs as upsampling modules in the task of point cloud upsampling by introducing two novel upsampling modules: Multi-branch GCN and Clone GCN. I show quantitatively and qualitatively the performance results of our novel and versatile upsampling modules when evaluated on a new proposed standardized dataset: PU600, which is the largest and most diverse point cloud upsampling dataset currently in the literature.
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